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BEGIN:VCALENDAR
CALSTYLE:GREGORIAN
PRODID:-//NL//Seminar Calendar//EN
VERSION:2.0
X-WR-CALNAME:NL
BEGIN:VTIMEZONE
TZID:America/Los_Angeles
X-LIC-LOCATION:America/Los_Angeles
BEGIN:DAYLIGHT
TZOFFSETFROM:-0800
TZOFFSETTO:-0700
TZNAME:PDT
DTSTART:19700308T020000
RRULE:FREQ=YEARLY;BYMONTH=3;BYDAY=2SU
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0700
TZOFFSETTO:-0800
TZNAME:PST
DTSTART:19701101T020000
RRULE:FREQ=YEARLY;BYMONTH=11;BYDAY=1SU
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DESCRIPTION: TBD
DTEND;TZID=America/Los_Angeles:20181109T160000
DTSTART;TZID=America/Los_Angeles:20181109T150000
LOCATION:11th Floor Large Conference Room [1135]
SUMMARY:TBD
UID:20181109T150000@NL
URL:http://www.isi.edu/natural-language/nl-seminar
END:VEVENT
BEGIN:VEVENT
DESCRIPTION: TBD
DTEND;TZID=America/Los_Angeles:20181012T160000
DTSTART;TZID=America/Los_Angeles:20181012T150000
LOCATION:11th Floor Large Conference Room [1135]
SUMMARY:TBD
UID:20181012T150000@NL
URL:http://www.isi.edu/natural-language/nl-seminar
END:VEVENT
BEGIN:VEVENT
DESCRIPTION: TBD
DTEND;TZID=America/Los_Angeles:20180907T160000
DTSTART;TZID=America/Los_Angeles:20180907T150000
LOCATION:11th Floor Large Conference Room [1135]
SUMMARY:TBD
UID:20180907T150000@NL
URL:http://www.isi.edu/natural-language/nl-seminar
END:VEVENT
BEGIN:VEVENT
DESCRIPTION: Can we detect the parts responsible for a generic behavior in a neural model to transfer it to another? In this talk, we first see why this might be a good idea, especially for low-resource machine translation. Then we focus on our approach to isolating a behavior. In our case, we specifically focus on coverage during machine translation. We present our results across different languages that show how neural models try to ensure coverage.Bio: Mozhdeh Gheini is a last-semester Computer Science master's student at USC Viterbi School of Engineering. At ISI NLP Group, she works on improving neural low-resource machine translation under the supervision of Jonathan May. She will be applying for Ph.D. programs this Fall.Abstract: In improvised comedy, saying "yes, and.. " is a rule-of-thumb that suggests that one person should accept the other person's offer (yes), and then add related information on top of that (and). Collecting a "yes, and.." corpus is not only helpful for building an improv agent, but can also be used for building conversational skill training tool, improving a dialogue system, etc. I will discuss the methods we have used for building such a dataset, data we have got so far and future considerations.Bio: Xinyu is a 2018 summer intern working with Dr. Jonathan May and Dr. Nanyun Peng on computerized improvised comedy. She will be joinging the Language Technologies Institute at Carnegie Mellon University in 2018 fall.
DTEND;TZID=America/Los_Angeles:20180824T160000
DTSTART;TZID=America/Los_Angeles:20180824T150000
LOCATION:11th Floor Large Conference Room [1135]
SUMMARY:T1. Constraints for Transfer Learning for Neural Machine Translation T2. Say Yes-and: Building a Specialized Corpus for Digital Improvised Comedy
UID:20180824T150000@NL
URL:http://www.isi.edu/natural-language/nl-seminar
END:VEVENT
BEGIN:VEVENT
DESCRIPTION: To generate language, we model what to say, why not also model how listeners will react? We show how pragmatic inference can be used to both generate and interpret natural language instructions for complex, sequential tasks. Our pragmatics-enabled models reason about how listeners will react upon hearing instructions, and reason counterfactually about why speakers produced the instructions they did. We find that this inference procedure improves state-of-the-art listener models (at correctly interpreting human instructions) and speaker models (at generating instructions correctly interpreted by humans) in diverse settings, including navigating through real-world indoor environments.Bio:Daniel Fried is a PhD student at UC Berkeley, working with Dan Klein on grounded semantics and structured prediction in natural language processing. Previously, he received a BS from the University of Arizona and an MPhil from the University of Cambridge. His work has been supported by a Churchill Scholarship, NDSEG Fellowship, Huawei / Berkeley AI Fellowship, and Tencent Fellowship.
DTEND;TZID=America/Los_Angeles:20180914T160000
DTSTART;TZID=America/Los_Angeles:20180914T150000
LOCATION:11th Floor Large Conference Room [1135]
SUMMARY:Pragmatic Models for Generating and Following Grounded Instructions
UID:20180914T150000@NL
URL:http://www.isi.edu/natural-language/nl-seminar
END:VEVENT
BEGIN:VEVENT
DESCRIPTION: Machine learning is at the forefront of many recent advances in natural language processing, enabled in part by the sophisticated models and algorithms that have been recently introduced. However, as a consequence of this complexity, machine learning essentially acts as a black-box as far as users are concerned. It is incredibly difficult to understand, predict, or "fix" the behavior of NLP models that have been deployed. In this talk, I propose interpretable representations that allow users and machine learning models to interact with each other: enabling machine learning models to provided explanations as to why a specific prediction was made and enabling users to inject domain knowledge into machine learning. The first part of the talk introduces an approach to estimate local, interpretable explanations for black-box classifiers and describes an approach to summarize the behavior of the classifier by selecting which explanations to show to the user. I will also briefly describe work on "closing the loop", i.e. allowing users to provide feedback on the explanations to improve the model, for the task of relation extraction, an important subtask of natural language processing. In particular, we introduce approaches to both explain the relation extractor using logical statements and to inject symbolic domain knowledge into relational embeddings to improve the predictions. I present experiments to demonstrate that an interactive interface is effective in providing users an understanding of, and an ability to improve, complex black-box machine learning systems.Bio: Sameer Singh is an Assistant Professor of Computer Science at the University of California, Irvine. He is working on large-scale and interactive machine learning applied to information extraction and natural language processing. Till recently, Sameer was a Postdoctoral Research Associate at the University of Washington. He received his PhD from the University of Massachusetts, Amherst in 2014, during which he also interned at Microsoft Research, Google Research, and Yahoo! Labs on massive-scale machine learning. He was selected as a DARPA Riser, was awarded the Adobe Research Data Science Award, won the grand prize in the Yelp dataset challenge, has been awarded the Yahoo! Key Scientific Challenges fellowship, and was a finalist for the Facebook PhD fellowship. Sameer has published more than 30 peer-reviewed papers at top-tier machine learning and natural language processing conferences and workshops.
DTEND;TZID=America/Los_Angeles:20170324T160000
DTSTART;TZID=America/Los_Angeles:20170324T150000
LOCATION:11th Floor Large Conference Room [1135]
SUMMARY:Intuitive Interactions with Black-box Machine Learning
UID:20170324T150000@NL
URL:http://www.isi.edu/natural-language/nl-seminar
END:VEVENT
BEGIN:VEVENT
DESCRIPTION: I am going to be talking about stuff that I have been working over thepast 6-9 months. This includes randomized algorithms and its applicationto 2 NLP problems: noun clustering and noun-pair clustering. I will alsobe commenting on my experience of working with very very large amounts ofreal Natural Language text (This includes processing and working with dataavailable from the web. This corpus is not the standard newspaper textthat we are so used to in the NLP community.) This talk will also cover alarge part of my thesis work.
DTEND;TZID=America/Los_Angeles:20050422T163000
DTSTART;TZID=America/Los_Angeles:20050422T150000
LOCATION:11 Large
SUMMARY:Working with Large Corpus, High speed clustering and its applications
UID:20050422T150000@NL
URL:http://www.isi.edu/natural-language/nl-seminar
END:VEVENT
BEGIN:VEVENT
DESCRIPTION:
DTEND;TZID=America/Los_Angeles:20030729T160000
DTSTART;TZID=America/Los_Angeles:20030729T150000
LOCATION:11 Small
SUMMARY:A Model of Word Movement for Machine Translation
UID:20030729T150000@NL
URL:http://www.isi.edu/natural-language/nl-seminar
END:VEVENT
BEGIN:VEVENT
DESCRIPTION: Many datasets for natural language processing problems lack linguistic variation, which hurts generalization of models trained on them. Recent research has shown that it is possible to break many learned models by evaluating them on adversarial examples, which are generated by manually introducing lexical, pragmatic, and syntactic variation to existing held-out examples from the data. Automating this process is challenging, as input semantics must be preserved in the face of potentially large sentence modifications. In this talk, I will focus specifically on syntactic variation in discussing our recent work on syntactically controlled paraphrase networks (SCPN) for adversarial example generation.Given a sentence and a target syntactic form (e.g., a constituency parse), an SCPN is trained to produce a paraphrase of the sentence with the desired syntax. We show it is possible to create training data for this task by first doing backtranslation at a very large scale, and then using a parser to label the syntactic transformations that naturally occur during this process. Such data allows us to train a neural encoder-decoder model with extra inputs to specify the target syntax. A combination of automated and human evaluations show that SCPNs generate paraphrases that almost always follow their target specifications without decreasing paraphrase quality when compared to baseline (uncontrolled) paraphrase systems. Furthermore, they are more capable of generating syntactically adversarial examples that both (1) "fool" pretrained models and (2) improve the robustness of these models to syntactic variation when used for data augmentation.Bio: Mohit Iyyer will be joining UMass Amherst as an assistant professor in Fall 2018. Currently, he is a Young Investigator at the Allen Institute of Artificial Intelligence; prior to that, he received a Ph.D. from the Department of Computer Science at the University of Maryland, College Park, advised by Jordan Boyd-Graber and Hal Daumé III. His research interests lie at the intersection of natural language processing and machine learning. More specifically, he focuses on designing deep neural networks for both traditional NLP tasks (e.g., question answering, language generation) and new problems that involve creative language (e.g., understanding narratives in novels). He has interned at MetaMind and Microsoft Research, and his research has won a best paper award at NAACL 2016 and a best demonstration award at NIPS 2015.
DTEND;TZID=America/Los_Angeles:20180330T160000
DTSTART;TZID=America/Los_Angeles:20180330T150000
LOCATION:11th Floor Large Conference Room [1135]
SUMMARY:Generating Adversarial Examples with Syntactically Controlled Paraphrase Networks
UID:20180330T150000@NL
URL:http://www.isi.edu/natural-language/nl-seminar
END:VEVENT
BEGIN:VEVENT
DESCRIPTION: Philipp Koehn and I will do a machine translation tutorial at ACL.Instead of an introductory tutorial, we'll do short 15-minute segmentson various hot topics in MT research. For the ISI NL seminar, I'llpresent 3 or 4 of those topics, determined by audience vote.
DTEND;TZID=America/Los_Angeles:20090710T160000
DTSTART;TZID=America/Los_Angeles:20090710T150000
LOCATION:11 Large
SUMMARY:Excerpts from ACL-09 Tutorial on "Topics in Machine Translation"
UID:20090710T150000@NL
URL:http://www.isi.edu/natural-language/nl-seminar
END:VEVENT
BEGIN:VEVENT
DESCRIPTION: In the 1990s, researchers applied their new developments in transducertheory using widely available easy-to-use toolkits for string transducers,and made well-known advances in parsing, machine translation, and otherareas. Rapid prototyping via software such as the AT&T toolkit and carmelwas useful for proofs of concept and in many cases led to unforseendevelopments in novel areas. In the current nlp research environment treebased strategies and new models have shown promising results in advancingthe state of the art, and recent developments in weighted tree automatatheory are enriching the bedrock created 40 years ago, but as of yet thereis no toolkit available with the necessary capabilities to turn promiseinto solution.Tiburon is the first probablistic tree transducer toolkit. Similar in formand function to the string-based toolkits of yesteryear, it is designed tobe easy to use, with simple but expressive definitions of tree automataand a concise set of vital operations that can be used to construct manyuseful tree-based nlp projects. Although a work in progress, Tiburon isalready a usable tool with active users between the ages of 6 and 41. Iwill describe the current status of the system, demonstrate ease of useand potential power, and discuss the challenges ahead.
DTEND;TZID=America/Los_Angeles:20060317T163000
DTSTART;TZID=America/Los_Angeles:20060317T150000
LOCATION:4th Floor
SUMMARY:Tiburon: A Finite State Tree Automata Toolkit
UID:20060317T150000@NL
URL:http://www.isi.edu/natural-language/nl-seminar
END:VEVENT
BEGIN:VEVENT
DESCRIPTION: Consider Donald Norman's quote, "The power of the unaided mind ishighly overrated. Without external aids, memory, thought, andreasoning are all constrained. But human intelligence is highlyflexible and adaptive, superb at inventing procedures and objects thatovercome its own limits. The real powers come from devising externalaids that enhance cognitive abilities." (Norman, 1993) Common methodsfor externalization include making sketches on whatever happens to behandy -- paper napkins, program margins, etc. -- and/or finding acolleague or two to discuss the problem with. It would seem then, thatvisualization and collaboration are natural possibilities for creatingpositive cognitive aids. I will discuss our approach to developinginteractive information visualizations both to support individuals andsmall groups of collaborators and briefly describe some of our recentresults.About the speaker:Sheelagh Carpendale holds a Canada Research Chair in InformationVisualization at the University of Calgary. Her research focuses onthe visualization, exploration and manipulation of information;visualizing such topics as ecological dynamics, uncertainty ininformation, social and communication information and investigatingthe development of information visualization environments that supportcollaboration. Dr. Carpendale's research in information visualizationand interaction design draws on her dual background in ComputerScience (BSc. and Ph.D. Simon Fraser University) and Visual Arts(Sheridan College, School of Design and Emily Carr, College of Art).
DTEND;TZID=America/Los_Angeles:20070504T163000
DTSTART;TZID=America/Los_Angeles:20070504T150000
LOCATION:11 Large
SUMMARY:Information Visualization and Collaboration
UID:20070504T150000@NL
URL:http://www.isi.edu/natural-language/nl-seminar
END:VEVENT
BEGIN:VEVENT
DESCRIPTION: This is two practice talks.-----------------------------------------------------------------------------FIRST TALK:The traditional approach to diagnosing learner speech errors in ComputerAided Language Learning is to create a linguistic profile of thelearner/user. We, however, propose that work must also be done to modelthe linguistic profile of a typcial native listener.Not all errors in second langage learner speech are created equal.Different errors sound more "severe" or "harsh" to native speaker ears andshould therefore be treated with more emphasis in pedagogical interaction.The Tactical Language Training System (TLTS) is a speech-enabledvirtual-reality based computer learning environment designed to teachArabic spoken communication to American English speakers. This talkaddresses the ways the TLTS contextualizes non-native speech errors, andhow this contextualization fits in the corrective exchanges between anon-native learner and a pedagogical agent built to model a nativelistener.The pedagogical system used in TLTS includes: * Automatic Speech Recognition (ASR) models which are built on acombination of both annnotated and unannotated non-native speech withnative speech data. * A stochastic generative model for errors in learner speech thatcreates mispronunciation grammars for the ASR * Reweighting of system-perceived mispronunciation severity based onaggregate native speaker judgements of quality pronunciation andintelligiblity. * Contextualization of feedback based on lexical and phoneticinventories of the native and non-native languages.-----------------------------------------------------------------------------SECOND TALK:We present a novel feature-enriched approach that learns to detect theconversation focus of threaded discussions by combining NLP analysis andIR techniques. Using the graph-based algorithm HITS, we integratedifferent features such as lexical similarity, poster trustworthiness, andspeech act analysis of human conversations with featureoriented linkgeneration functions. It is the first quantitative study to analyze humanconversation focus in the context of online discussions that takes intoaccount heterogeneous sources of evidence. Experimental results using athreaded discussion corpus from an undergraduate class show that itachieves significant performance improvements compared with the baselinesystem.
DTEND;TZID=America/Los_Angeles:20060512T163000
DTSTART;TZID=America/Los_Angeles:20060512T150000
LOCATION:11 Large
SUMMARY:Pedagogical Contextualization of Language Learner Speech Errors AND Learning to Detect Conversation Focus of Threaded Discussions
UID:20060512T150000@NL
URL:http://www.isi.edu/natural-language/nl-seminar
END:VEVENT
BEGIN:VEVENT
DESCRIPTION: Social networks have many counter-intuitive properties, including the âfriendship paradoxâ that states, on average, your friends have more friends than you do. Recently, a variety of other paradoxes were demonstrated in online social networks. This paper explores the origins of these network paradoxes. Specifically, we ask whether they arise from mathematical properties of the networks or whether they have a behavioral origin. We show that sampling from fat-tailed distributions always gives rise to a paradox in the mean, but not the median. We propose a strong form of network paradoxes, based on utilizing the median, and validate it empirically using data from two online social networks. Specifically, we show that for any user the majority of userâs friends and followers have more friends, followers, etc. than the user, and that this cannot be explained by statistical properties of sampling. Next, we explore the behavioral origins of the paradoxes by using the shuffle test to remove correlations between node degrees and attributes. We find that paradoxes for the mean persist in the shuffled network, but not for the median. We demonstrate that strong paradoxes arise due to the assortativity of user attributes, including degree, and correlation between degree and attribute.
DTEND;TZID=America/Los_Angeles:20140411T160000
DTSTART;TZID=America/Los_Angeles:20140411T150000
LOCATION:11th Floor Large Conference Room [1135]
SUMMARY:Network Weirdness: Exploring the Origins of Network Paradoxes
UID:20140411T150000@NL
URL:http://www.isi.edu/natural-language/nl-seminar
END:VEVENT
BEGIN:VEVENT
DESCRIPTION: The dialogue policy of a dialogue system decides on what dialogue move (also called âactionâ) the system should make given the dialogue context (also called âdialogue stateâ). Building hand-crafted dialogue policies is a hard task, and there is no guarantee that the resulting policies will be optimal. This issue has motivated the dialogue community to use statistical methods for automatically learning dialogue policies, the most popular of which is reinforcement learning (RL). However, to date, RL has mainly been used to learn dialogue policies in slot-filling applications (e.g., restaurant recommendation, flight reservation, etc.) largely ignoring other more complex genres of dialogue such as negotiation. This talk presents challenges in reinforcement learning of negotiation dialogue policies. The first part of the talk focuses on applying RL to a two-party multi-issue negotiation domain. Here the main challenges are the very large state and action space, and learning negotiation dialogue policies that can perform well for a variety of negotiation settings, including against interlocutors whose behavior has not been observed before. Good negotiators try to adapt their behaviors based on their interlocutorsâ behaviors. However, current approaches to using RL for dialogue management assume that the userâs behavior does not change over time. In the second part of the talk, I will present an experiment that deals with this problem in a resource allocation negotiation scenario.Kallirroi Georgila is a Research Assistant Professor at the Institute for Creative Technologies (ICT) at the University of Southern California (USC) and at USCâs Computer Science Department. Before joining USC/ICT in 2009 she was a Research Scientist at the Educational Testing Service (ETS) and before that a Research Fellow at the School of Informatics at the University of Edinburgh. Her research interests include all aspects of spoken dialogue processing with a focus on reinforcement learning of dialogue policies, expressive conversational speech synthesis, and speech recognition. She has served on the organizing, senior, and program committees of many conferences and workshops. Her research work is funded by the National Science Foundation and the Army Research Office.
DTEND;TZID=America/Los_Angeles:20170421T160000
DTSTART;TZID=America/Los_Angeles:20170421T150000
LOCATION:11th Floor Large Conference Room [1135]
SUMMARY:Reinforcement learning of negotiation dialogue policies
UID:20170421T150000@NL
URL:http://www.isi.edu/natural-language/nl-seminar
END:VEVENT
BEGIN:VEVENT
DESCRIPTION: Automatic word alignment is the problem of automatically annotatingparallel text with translational correspondence. Previous generativeword alignment models have made structural assumptions such as the1-to-1, 1-to-N, or phrase-based consecutive word assumptions, whileprevious discriminative models have either made one of theseassumptions directly or used features derived from a generative modelusing one of these assumptions. We present a new generative alignmentmodel which avoids these structural limitations, and show that it iseffective when trained using both unsupervised and semi-supervisedtraining methods. Experiments show strong improvements in wordalignment accuracy and usage of the generated alignments inhierarchical and phrasal SMT systems improves the BLEU score.
DTEND;TZID=America/Los_Angeles:20070615T110000
DTSTART;TZID=America/Los_Angeles:20070615T103000
LOCATION:11 Large
SUMMARY:Getting the structure right for word alignment: LEAF
UID:20070615T103000@NL
URL:http://www.isi.edu/natural-language/nl-seminar
END:VEVENT
BEGIN:VEVENT
DESCRIPTION: Tree-based probability models of translation have been proposed to takeadvantage of parse trees on one, both, or neither sides of a parallelcorpus. I will present comparative results for these three approaches forthe task of word alignment on Chinese-English and French-English data, aswell as some analysis of what is going on behind the numbers.
DTEND;TZID=America/Los_Angeles:20040625T160000
DTSTART;TZID=America/Los_Angeles:20040625T150000
LOCATION:11 Large
SUMMARY:Syntactic Supervision and Tree-Based Alignment
UID:20040625T150000@NL
URL:http://www.isi.edu/natural-language/nl-seminar
END:VEVENT
BEGIN:VEVENT
DESCRIPTION: Various types of how-to-knowledge are encoded in natural language instructions: from setting up a tent, to preparing a dish for dinner, and to executing biology lab experiments. These types of instructions are based on procedural language, which poses unique challenges. For example, verbal arguments are commonly elided when they can be inferred from context, e.g., ``bake for 30 minutes'', not specifying bake what and where. Entities frequently merge and split, e.g., ``vinegarââ and ``oilââ merging into ``dressingââ, creating challenges to reference resolution. And disambiguation often requires world knowledge, e.g., the implicit location argument of ``stir frying'' is on ``stove''. In this talk, I will present our recent approaches to interpreting and composing cooking recipes that aim to address these challenges.In the first part of the talk, I will present an unsupervised approach to interpreting recipes as action graphs, which define what actions should be performed on which objects and in what order. Our work demonstrates that it is possible to recover action graphs without having access to gold labels, virtual environments or simulations. The key insight is to rely on the redundancy across different variations of similar instructions that provides the learning bias to infer various types of background knowledge, such as the typical sequence of actions applied to an ingredient, or how a combination of ingredients (e.g., ``flour'', ``milk'', ``eggs'') becomes a new entity (e.g, ``wet mixture'').In the second part of the talk, I will present an approach to composing new recipes given a target dish name and a set of ingredients. The key challenge is to maintain global coherence while generating a goal-oriented text. We propose a Neural Checklist Model that attains global coherence by storing and updating a checklist of the agenda (e.g., an ingredient list) with paired attention mechanisms for tracking what has been already mentioned and what needs to be yet introduced. This model also achieves strong performance on dialogue system response generation. I will conclude the talk by discussing the challenges in modeling procedural language and acquiring the necessary background knowledge, pointing to avenues for future research.Bio:Yejin Choi is an assistant professor at the Computer Science & Engineering Department of University of Washington. Her recent research focuses on language grounding, integrating language and vision, and modeling nonliteral meaning in text. She was among the IEEEâs AI Top 10 to Watch in 2015 and a co-recipient of the Marr Prize at ICCV 2013. Her work on detecting deceptive reviews, predicting the literary success, and learning to interpret connotation has been featured by numerous media outlets including NBC News for New York, NPR Radio, New York Times, and Bloomberg Business Week. She received her Ph.D. in Computer Science at Cornell University.
DTEND;TZID=America/Los_Angeles:20161202T160000
DTSTART;TZID=America/Los_Angeles:20161202T150000
LOCATION:11th Floor Large Conference Room [1135]
SUMMARY:Procedural Language and Knowledge
UID:20161202T150000@NL
URL:http://www.isi.edu/natural-language/nl-seminar
END:VEVENT
BEGIN:VEVENT
DESCRIPTION: The topics & approximate start times:(3:00 sharp) My 7-10 min bit for panel discussion on "Manual vs. AutomatedKnowledge Acquisition"Will touch on web extraction vs. learning from volunteers -- strengths andweaknesses, new thoughts on synergies(3:15) Designing Intelligent Acquisition Interfaces for Collecting WorldKnowledge from Web Contributors(paper by Timothy Chklovski, Yolanda Gil)(3:55) Collecting Paraphrase Corpora from Volunteer Contributors (paper byTimothy Chklovski)
DTEND;TZID=America/Los_Angeles:20050929T163000
DTSTART;TZID=America/Los_Angeles:20050929T150000
LOCATION:11 Large
SUMMARY:Previews of my talks for K-CAP
UID:20050929T150000@NL
URL:http://www.isi.edu/natural-language/nl-seminar
END:VEVENT
BEGIN:VEVENT
DESCRIPTION: We develop a system that lets people overcome language barriers by letting them speak a language they do not know. Our system accepts text entered by a user, translates the text, then converts the translation into a phonetic spelling in the userâs own orthography. We trained the system on phonetic spellings in travel phrasebooks.Xing Shi is a PhD student at USC, advised by Professor Kevin Knight.
DTEND;TZID=America/Los_Angeles:20140523T160000
DTSTART;TZID=America/Los_Angeles:20140523T150000
LOCATION:11th Floor Large Conference Room [1135]
SUMMARY:How to Speak a Language Without Knowing It
UID:20140523T150000@NL
URL:http://www.isi.edu/natural-language/nl-seminar
END:VEVENT
BEGIN:VEVENT
DESCRIPTION: We present a grand challenge to build a corpus that will include all of the world's languages, in a consistent structure that permits large-scale cross-linguistic processing, enabling the study of universal linguistics. The focal data types, bilingual texts and lexicons, relate each language to one of a set of reference languages. We propose that the ability to train systems to translate into and out of a given language be the yardstick for determining when we have successfully captured a language. We call on the computational linguistics community to begin work on this Universal Corpus, pursuing the many strands of activity described here, as their contribution to the global effort to document the world's linguistic heritage before more languages fall silent.(This talk will present joint work with Steve Abney.)Brief Bio:Steven Bird is Associate Professor in the Department of ComputerScience and Software Engineering at the University of Melbourne, andalso Senior Research Associate at the Linguistic Data Consortium. In2009 he served as president of the Association for ComputationalLinguistics, and he completed a textbook on Natural LanguageProcessing, published by O'Reilly. Steven studies scalable,semi-automatic methods for analyzing spoken and written language, andfor preserving endangered languages. This involves a mixture ofcomputational modelling and linguistic fieldwork. For further detailsand online publications, please visit http://stevenbird.me/
DTEND;TZID=America/Los_Angeles:20100609T163000
DTSTART;TZID=America/Los_Angeles:20100609T153000
LOCATION:10th Floor Conference Room
SUMMARY:The Human Language Project: Building a Universal Corpus of the World's Languages
UID:20100609T153000@NL
URL:http://www.isi.edu/natural-language/nl-seminar
END:VEVENT
BEGIN:VEVENT
DESCRIPTION: What is in common, and what is different, between translating from English to Chinese and compiling C++ into machine code?In this talk I will first introduce a tree-based (aka syntax-directed) paradigm for machine translation, inspired by both human translators and compilers. In this paradigm, a source language sentence is first parsed into a syntactic tree, which is then recursively converted into a target language sentence via tree-to-string transformation rules. Since the translation process is driven by the syntax, this approach resembles the classical "syntax-directed translation" method in compiling theory.However, natural languages are crucially different from programming languages in that they are fundamentally ambiguous. So we don't (and will probably never) have perfect parsers, and parsing errors adversely affect translation quality. To alleviate this problem, an obvious idea is to use the top-k parses, rather than a single 1-best, but this only helps a little bit due to the limited scope of the k-best list. We instead propose a "forest-based approach", which translates a packed forest encoding *exponentially* many parses in a compact (polynomial) space by sharing common subtrees. Large-scale experiments showed very significant improvements (over the 1-best baseline) in terms of translation quality, which outperforms the best reported systems to date. More interestingly, translating a forest of millions of trees is even faster than translating on top-30 individual trees thanks to dynamic programming.This talk includes joint work with Kevin Knight and Aravind Joshi (first part), and with Haitao Mi and Qun Liu (second/third parts).Short Bio:Liang Huang recently completed his PhD study at the University of Pennsylvania, co-supervised by Aravind Joshi and Kevin Knight (USC/ISI). He is mainly interested in the theoretical aspects of computational linguistics, in particular, efficient algorithms in parsing and machine translation, generic dynamic programming, and formal properties of synchronous grammars. His thesis develops a set of "forest-based methods" that have been applied to many problems in NLP including k-best parsing, forest rescoring and reranking, and forest-based translation. His awards include an Outstanding Paper Award at ACL 2008, and a University Teaching Award at Penn in 2005.http://www.cis.upenn.edu/~lhuang3/
DTEND;TZID=America/Los_Angeles:20081217T160000
DTSTART;TZID=America/Los_Angeles:20081217T150000
LOCATION:4th Floor CR
SUMMARY:Tree-based and Forest-based Translation
UID:20081217T150000@NL
URL:http://www.isi.edu/natural-language/nl-seminar
END:VEVENT
BEGIN:VEVENT
DESCRIPTION: Automatic Natural Language applications often require the processing ofstructured data. Traditional machine learning approaches attempt torepresent structured syntactic/semantic objects by means of flat featurerepresentations, i.e. attribute-value vectors. This raises two problems:1. There is no well defined theoretical motivation for such feature model.Structural properties may not fit in any flat feature representation.2. To define effective flat features, a deep knowledge about thelinguistic phenomenon is required.Kernel methods for Natural Language Processing aim to solve both the aboveproblems as kernel functions can be used to define similarities betweenlinguistic objects without explicitly defining the target feature space.In this way, a linguistic phenomenon can be modeled at a more abstractlevel where the modeling is easier. Such property is extremely useful whenthe representation of linguistic phenomena is still not well understood.For example, the feature design of semantic role labeling appear to bequite complex since several and non-definitive feature sets have beenproposed.As a viable alternative to manual feature design, kernel methods proposetwo steps: (1) they generate all substructures of the targetsyntactic/semantic structures and (2) they let the learning algorithm(e.g. Support Vector Machines) to select the most relevant substructures.In this talk, we (1) introduce the PropBank and FrameNet predicateargument structures, (2) present the standard approaches to the automaticlabeling of semantic roles and (3) show advanced semantic role labelingmodels based on kernel methods.About the speaker:Alessandro Moschitti is a researcher at the Computer Science Department ofthe University of Rome ^ÃTor Vergata^Ã. In 1998 he took his master degreein Computer Science at the University of Rome ^ÃLa Sapienza^Ã. In 2003 hefinished his PhD in Computer Science at ^ÃTor Vergata^Ã University.Between 2002 and 2004 he worked as an associate researcher in theUniversity of Texas at Dallas. His research interests concern machinelearning approaches for Natural Language Processing and InformationRetrieval. His deep expertise relates to automated text categorization andsemantic role labeling. Recently, he has devised new kernels which enableSupport Vector and other kernel-based machines to carry out advancedsemantic processing.
DTEND;TZID=America/Los_Angeles:20050706T153000
DTSTART;TZID=America/Los_Angeles:20050706T140000
LOCATION:11 Large
SUMMARY:Kernel Methods for Semantic Role Labeling
UID:20050706T140000@NL
URL:http://www.isi.edu/natural-language/nl-seminar
END:VEVENT
BEGIN:VEVENT
DESCRIPTION: We propose a simple generative syntactic language model that conditions on overlapping tree contexts in the same way that n-gram language models condition on overlapping sentence context. We estimate the parameters of our model by collecting counts from automatically parsed text using standard n-gram language model estimation techniques, allowing us to train a model on over one billion tokens of data using a single machine in a mater of hours. We evaluate on a range of grammaticality tasks, and find that we consistently outperform n-gram models and other generative baselines, and even compete with state-of-the-art discriminative models hand-designed for each task, despite training on positive data alone. We also show some improvements in preliminary machine translation experiments.
DTEND;TZID=America/Los_Angeles:20120217T160000
DTSTART;TZID=America/Los_Angeles:20120217T150000
LOCATION:11th Floor Large Conference Room [1135]
SUMMARY:Large Scale Syntactic Language Modeling with Treelets
UID:20120217T150000@NL
URL:http://www.isi.edu/natural-language/nl-seminar
END:VEVENT
BEGIN:VEVENT
DESCRIPTION: Abstract: Designing 3D scenes is currently a creative task that requires significant expertise and effort in using complex 3D design interfaces. This design process starts in contrast to the easiness with which people can use language to describe real and imaginary environments. We present an interactive text to 3D scene generation system that allows a user to design 3D scenes using natural language. A user provides input text from which we extract explicit constraints on the objects that should appear in the scene. Given these explicit constraints, the system then uses a spatial knowledge base learned from an existing database of 3D scenes and 3D object models to infer an arrangement of the objects forming a natural scene matching the input description. Using textual commands the user can then iteratively refine the created scene by adding, removing, replacing, and manipulating objects.Bio: Angel Chang recently received her PhD after working in the Stanford NLP group where she was advised by Chris Manning. Her research focuses on the intersection of natural language understanding, computer graphics, and AI. She is currently a visiting expert at Tableau Research. More details at http://stanford.edu/~angelx/Webcast link: http://webcasterms1.isi.edu/mediasite/Viewer/?peid=735bfbb4ba1a4b749fe591958f837ccb1d
DTEND;TZID=America/Los_Angeles:20160226T160000
DTSTART;TZID=America/Los_Angeles:20160226T150000
LOCATION:11th Floor Large Conference Room [1135]
SUMMARY:Interactive scene design using natural language
UID:20160226T150000@NL
URL:http://www.isi.edu/natural-language/nl-seminar
END:VEVENT
BEGIN:VEVENT
DESCRIPTION: In this talk I will examine problems encountered in coming to somekind of understanding of one sonnet by Shakespeare (his 64th), askwhat it would take to solve these problems computationally, andsuggests routes to the solution. The general conclusion is that weare closer to this goal as one might think. Or are we?Bio:Jerry Hobbs is famous primarily for having an office next to KevinKnight's and a parking space next to Ed Hovy's. He has readeverything of Shakespeare's that survives, including his will andplays of dubious authorship. But that was all a long time ago.
DTEND;TZID=America/Los_Angeles:20061215T163000
DTSTART;TZID=America/Los_Angeles:20061215T150000
LOCATION:11 Large
SUMMARY:When Will Computers Understand Shakespeare?
UID:20061215T150000@NL
URL:http://www.isi.edu/natural-language/nl-seminar
END:VEVENT
BEGIN:VEVENT
DESCRIPTION: In this talk, I will focus on the importance of integrating knowledge of human speech production and speech perception mechanisms, and language-specific information with statistically-based, data-driven approaches to develop robust and scalable speech processing algorithms. The need for such hybrid systems is especially critical when dealing with data corrupted by background acoustic noise, when training data are limited, and when dealing with accents.
DTEND;TZID=America/Los_Angeles:20130201T160000
DTSTART;TZID=America/Los_Angeles:20130201T150000
LOCATION:11th Floor Large Conference Room [1135]
SUMMARY:Dealing with Limited and Noisy Data in Speech Processing: A Hybrid Knowledge-Based and Statistical Approach
UID:20130201T150000@NL
URL:http://www.isi.edu/natural-language/nl-seminar
END:VEVENT
BEGIN:VEVENT
DESCRIPTION: Test collections for information retrieval tasks have traditionallyassumed that what we are searching for are documents (e.g., Web pages,news stories, or academic documents). Most information that is generatedis, however, not in originally generated as part of a document, but ratheras what we might refer to as "conversational media" (e.g., email, speech,or instant messaging). In this talk, I'll describe the creation of twotest collections for conversational media, an email collection beingcreated in the TREC Enterprise Search track and a spoken word testcollection for the the Cross-Language Evaluation Forum (CLEF). I'll spendmost of the talk describing the details of the CLEF test collection,illustrating the issues with some of the results that we have obtainedfrom our experiments with that collection. I'll conclude with a fewremarks about the implications of what we are learning for DARPA's newGALE program. This is joint work with Charles University, the IBM TJWatson Research Center, the Johns Hopkins University, the Survivors of theShoah Visual History Foundation, and the University of West Bohemia.About the speaker:Douglas Oard is an Associate Professor at the University of Maryland,College Park, with a joint appointment in the College of InformationStudies and the Institute for Advanced Computer Studies. He holds a Ph.D.in Electrical Engineering from the University of Maryland, and hisresearch interests center around the use of emerging technologies tosupport information seeking by end users. In 2002 and 2003, Doug spent ayear in paradise here at USC-ISI. His recent work has focused oninteractive techniques for cross-language information retrieval and onsearching conversational text and speech. Additional information isavailable at http://www.glue.umd.edu/~oard/.
DTEND;TZID=America/Los_Angeles:20050805T163000
DTSTART;TZID=America/Los_Angeles:20050805T150000
LOCATION:11 Large
SUMMARY:The CLEF Cross-Language Speech Retrieval Test Collection
UID:20050805T150000@NL
URL:http://www.isi.edu/natural-language/nl-seminar
END:VEVENT
BEGIN:VEVENT
DESCRIPTION: The last decade has seen a plethora of papers in NLP devoted to MachineLearning algorithms. However, most of these papers have devoted theireffort exclusively to improving the system performance on the accuracyaxis. Most of the sophisticated NLP algorithms are extremely slow and donot scale up easily when applied to large amounts of data.I will talk about the importance of randomized algorithms and theirpotential in speeding up some NLP algorithms. This talk will be a surveyof some recent advances in Theoretical Computer Science/Math seen with anNLP point-of-view. I am not going to present any results. But I am hopingthat this talk will clarify my thinking process, get feedback from peopleand help me colloborate with others.
DTEND;TZID=America/Los_Angeles:20040813T163000
DTSTART;TZID=America/Los_Angeles:20040813T150000
LOCATION:11 Large
SUMMARY:Randomized algorithms and its application to NLP
UID:20040813T150000@NL
URL:http://www.isi.edu/natural-language/nl-seminar
END:VEVENT
BEGIN:VEVENT
DESCRIPTION: In this talk, I'll present the investigation I'm carrying out in ISIlately under Daniel Marcu's supervision. Following the noisy-channelframework, we propose a statistical model for learning the argumentstructures of verbs automatically. We show that we are able to learn bothlexicalized and generalized structures and achieve good results, relyingonly on basic NLP tools like a POS tagger and named-entity recognizer. Wealso present a comparison of the structures we learn with the predictedones in PropBank.
DTEND;TZID=America/Los_Angeles:20041115T163000
DTSTART;TZID=America/Los_Angeles:20041115T150000
LOCATION:8th floor multipurpose room (#849) -- NOT the conference room
SUMMARY:Unsupervised learning of verb argument structures
UID:20041115T150000@NL
URL:http://www.isi.edu/natural-language/nl-seminar
END:VEVENT
BEGIN:VEVENT
DESCRIPTION: Composition of Tree TransducersSince finite state (string) transducers are not expressive enough for many NLPapplications, computational linguistics started to investigate treetransducers for the task of machine translation, for example. Quite somesuccessful work has been done on generalizing results from string transducersto tree transducers. But when it comes to composition results are notsatisfying because generally tree transducers are not closed undercomposition. Still we think that most of the tree transducers used in NLP arecomposable and that is why we defined the problem of the composition for twoindividual transducers instead of the whole class. During the summer westarted with linear nondeleting tree transducers with epsilon rules andapproached an algorithm to decide for two such transducers whether theircomposition is again in the same class.Using the Perceptron Algorithm to Tune Large Numbers of Feature Weights for Syntax-Based Statistical Machine TranslationCurrent state-of-the-art syntax-based statistical machine translationsystems produce many candidate translations out of which the output translationis selected by taking the argmax over all candidates i of <w,f_i> where w is aweight vector and f_i is a vector of the feature values for candidate i. Thefeatures used by the system and their corresponding weights have a major impacton a system's performance. Currently, Minimum Error Rate Training (MERT) is used totune the weights of the features. A drawback of this is that it isn't tractableto tune large numbers of feature weights. I will discuss using the perceptronalgorithm to tune feature weights for statistical machine translation. If I get interestingresults before my talk, I may also dicsuss new classes of features (potentially very largenumbers of features) that can be used for improving MT performance.
DTEND;TZID=America/Los_Angeles:20070829T163000
DTSTART;TZID=America/Los_Angeles:20070829T150000
LOCATION:11 Large
SUMMARY:Summer Intern Presentations: Composition of Tree Transducers AND Using the Perceptron Algorithm to Tune Large Numbers of Feature Weights for Syntax-Based Statistical Machine Translation
UID:20070829T150000@NL
URL:http://www.isi.edu/natural-language/nl-seminar
END:VEVENT
BEGIN:VEVENT
DESCRIPTION: Automatic word alignment plays a critical role in statistical machinetranslation. Unfortunately the relationship between alignment quality andstatistical machine translation performance has not been well understood.In the recent literature the alignment task has frequently been decoupledfrom the translation task, and assumptions have been made about measuringalignment quality for machine translation which, it turns out, are notjustified. In particular, none of the tens of papers published over thelast five years has shown that significant decreases in Alignment ErrorRate (AER) result in significant increases in translation quality. I willexplain this state of affairs and present steps towards measuringalignment quality in a way which is predictive of statistical machinetranslation quality.I will also provide a brief overview of some of my other work on trainingand search for word alignment.
DTEND;TZID=America/Los_Angeles:20060203T163000
DTSTART;TZID=America/Los_Angeles:20060203T150000
LOCATION:11 Large
SUMMARY:Measuring Word Alignment Quality for Statistical Machine Translation
UID:20060203T150000@NL
URL:http://www.isi.edu/natural-language/nl-seminar
END:VEVENT
BEGIN:VEVENT
DESCRIPTION: My presentation will overview recent activities on Chinese-English SMTcarried out at ITC-irst (Trento, Italy). After an overview of thecomplete architecture of our system, I will focus on progress made inChinese word-segmentation, phrase-based modeling and decoding, log-linearmodeling and minimum error training, and language model adaptation.Experimental results will be provided in terms of Bleu and Nist scores ontwo translation tasks: basic traveling expressions and news reports,respectively adopted by the C-STAR consortium and for the 2002 and 2003NIST MT evaluation campaigns.Bio:Marcello Federico has been a permanent researcher at ITC-irst since 1991.During 1998-2003, he led the "Multilingual natural speech technologies"(MUNST) research line at ITC-irst. Since 2004, he is head of the"Cross-language information processing" (Hermes) research line. Hisinterests include automatic speech recognition, statistical languagemodeling, information retrieval, and machine translation.
DTEND;TZID=America/Los_Angeles:20040617T163000
DTSTART;TZID=America/Los_Angeles:20040617T150000
LOCATION:4th Floor
SUMMARY:Statistical Machine Translation at ITC-irst
UID:20040617T150000@NL
URL:http://www.isi.edu/natural-language/nl-seminar
END:VEVENT
BEGIN:VEVENT
DESCRIPTION: Jointly parsing two languages has been shown to improve accuracies oneither or both sides. However, its search space is much bigger thanthe monolingual case, forcing existing approaches to employcomplicated modeling and crude approximations. Here we propose a muchsimpler alternative, bilingually-constrained monolingual parsing,where a source-language parser learns to exploit reorderings asadditional observation, but not bothering to build the target-sidetree as well. We show specifically how to enhance a shift-reducedependency parser to use alignment features to resolve shift-reduceconflicts. Experiments on the bilingual portion of Chinese Treebankshow that, with just 3 bilingual features, we can improve parsingaccuracies by 0.6% for both English and Chinese, with negligible (~6%)efficiency overhead, thus much faster than biparsing.http://www.cis.upenn.edu/~lhuang3/biparsing.pdf
DTEND;TZID=America/Los_Angeles:20090821T161500
DTSTART;TZID=America/Los_Angeles:20090821T150000
LOCATION:4th Floor Conference Room
SUMMARY:Bilingually-Constrained (Monolingual) Shift-Reduce Parsing
UID:20090821T150000@NL
URL:http://www.isi.edu/natural-language/nl-seminar
END:VEVENT
BEGIN:VEVENT
DESCRIPTION: This talk summarizes our experience with searching for small modelsfor syntax-based machine translation. I will first present casessuggesting that smaller models are desirable, and present someevidence that minimizing model size is a reasonable objectivefunction. I will then show cases where this objective may be tooaggressive.
DTEND;TZID=America/Los_Angeles:20100827T160000
DTSTART;TZID=America/Los_Angeles:20100827T153000
LOCATION:11th Floor Large Conference Room [1135]
SUMMARY:Intern Final Talk: Small is beautiful. Is it any good?
UID:20100827T153000@NL
URL:http://www.isi.edu/natural-language/nl-seminar
END:VEVENT
BEGIN:VEVENT
DESCRIPTION: Neural Machine Translation is powerful but we know little about the black box. We conduct the following two investigations to gain a better understanding: First, we investigate how neural, encoder-decoder translation systems output target strings of appropriate lengths, finding that a collection of hidden units learns to explicitly implement this functionality. Second, we investigate whether a neural, encoderdecoder translation system learns syntactic information on the source side as a by-product of training. We propose two methods to detect whether the encoder has learned local and global source syntax. A fine-grained analysis of the syntactic structure learned by the encoder reveals which kinds of syntax are learned and which are missing.Bio: Xing Shi is a PhD student at ISI working with Prof. Kevin Knight.
DTEND;TZID=America/Los_Angeles:20161014T160000
DTSTART;TZID=America/Los_Angeles:20161014T150000
LOCATION:6th Floor Large Conference Room [689]
SUMMARY:EMNLP practice talk: Understanding Neural Machine Translation: length control and syntactic structure
UID:20161014T150000@NL
URL:http://www.isi.edu/natural-language/nl-seminar
END:VEVENT
BEGIN:VEVENT
DESCRIPTION: I present my summer project - writing rule-based software forsimplifying texts. Task definition and motivations will bediscussed, as well as human and automatic evaluation, thelatter using a question answering system.This is joint work with Daniel Marcu and Kevin Knight.
DTEND;TZID=America/Los_Angeles:20030915T160000
DTSTART;TZID=America/Los_Angeles:20030915T143000
LOCATION:11 Large
SUMMARY:Analyzing Sentences into Facts: Simple is Beautiful
UID:20030915T143000@NL
URL:http://www.isi.edu/natural-language/nl-seminar
END:VEVENT
BEGIN:VEVENT
DESCRIPTION: Large corpora of parsed sentences with semantic role labels (e.g. PropBank)provide training data for use in the creation of high-performance automaticsemantic role labeling systems. Despite the size of these corpora,individual verbs (or rolesets) often have only a handful of instances inthese corpora, and only a fraction of English verbs have even a singleannotation. In this paper, we describe an approach for dealing with thissparse data problem, enabling accurate semantic role labeling for novelverbs (rolesets) with only a single training example. Our approach involvesthe identification of syntactically similar verbs found in PropBank, thealignment of arguments in their corresponding rolesets, and the use of theircorresponding annotations in PropBank as surrogate training data.
DTEND;TZID=America/Los_Angeles:20070601T160000
DTSTART;TZID=America/Los_Angeles:20070601T153000
LOCATION:11 Large
SUMMARY:Generalizing Semantic Role Annotations Across Syntactically Similar Verbs
UID:20070601T153000@NL
URL:http://www.isi.edu/natural-language/nl-seminar
END:VEVENT
BEGIN:VEVENT
DESCRIPTION: Previous research has indicated that when a polysemous word appears twoor more times in a discourse, it is extremely likely that they will allshare the same sense (Gale et al. 92). However, those results werebased on a coarse-grained distinction between senses (e.g, {\emsentence} in the sense of a `prison sentence' vs. a `grammaticalsentence'). I conducted an analysis of multiple senses within twosense-tagged corpora, Semcor and DSO. These corpora used WordNet fortheir sense inventory. I found significantly more occurrences ofmultiple-senses per discourse than reported in (Gale et al. 92) (33\%instead of 4\%). I also found classes of ambiguous words in which asmany as 45\% of the senses in the class co-occur within a document. Iwill discuss the implications of these results for the task ofword-sense tagging and for the way in which senses should berepresented.
DTEND;TZID=America/Los_Angeles:20031219T163000
DTSTART;TZID=America/Los_Angeles:20031219T150000
LOCATION:11 Large
SUMMARY:More than One Sense Per Discourse
UID:20031219T150000@NL
URL:http://www.isi.edu/natural-language/nl-seminar
END:VEVENT
BEGIN:VEVENT
DESCRIPTION: The Mouse Genome Informatics database (MGI) has participated extensively in shared NLP challenges focussed on developing infrastructure for their use. This collaboration has advanced the field of applying NLP to biomedical text but has not yet generated workable technology for use in the lab. In advance of a workshop (Monday August 19, 2013 at ISI) dedicated to this subject, I will describe the SciKnowMine project to introduce the domain of biomedical NLP and to showcase how we can collaboratively accelerate the process of biocuration, making these important databases far more effective.Students, colleagues! You are very welcome to the workshop: <a href=http://www.isi.edu/projects/sciknowmine/sciknowmine_release_workshop_-_bridging_bionlp_and_biocuration>http://www.isi.edu/projects/sciknowmine/sciknowmine_release_workshop_-_bridging_bionlp_and_biocuration</a>
DTEND;TZID=America/Los_Angeles:20130816T160000
DTSTART;TZID=America/Los_Angeles:20130816T150000
LOCATION:11th Floor Large Conference Room [1135]
SUMMARY:Bridging Between Bioinformatics and Natural Language Processing
UID:20130816T150000@NL
URL:http://www.isi.edu/natural-language/nl-seminar
END:VEVENT
BEGIN:VEVENT
DESCRIPTION: NLP applications such as Question Answering (QA), Information Extraction (IE), or Machine Translation (MT) are incorporating increasing amounts of semantic information. A fundamental building block of semantic information is the relation between a predicate and its arguments, e.g. eat(John,burger). In order to reason at higher levels of abstraction, it is useful to group relation instances according to the types of their predicates and the types of their arguments. For example, while eat(Mary,burger) and devour(John,tofu) are two distinct relation instances, they share the underlying predicate and argument types INGEST(PERSON,FOOD).A central question is: where do the types and relations come from?The subfield of NLP concerned with this is relation extraction, which comprises two main tasks:1. identifying and extracting relation instances from text2. determining the types of their predicates and argumentsThe first task is difficult for several reasons. Relations can express their predicate explicitly or implicitly. Furthermore, their elements can be far part, with unrelated words intervening. In this thesis, we restrict ourselves to relations that are explicitly expressed between syntactically related words. We harvest the relation instances from dependency parses.The second task is the central focus of this thesis. Specifically, we will address these three problems: 1) determining argument types 2) determining predicate types 3) determining argument and predicate types. For each task, we model predicate and argument types as latent variables in a hidden Markov models. Depending on the type system available for each of these tasks, our approaches range from unsupervised to semi-supervised to fully supervised training methods.The central contributions of this thesis are as follows:1. Learning argument types (unsupervised): We present a novel approach that learns the type system along with the relation candidates when neither is given. In contrast to previous work on unsupervised relation extraction, it produces human-interpretable types rather than clusters. We also investigate its applicability to downstream tasks such as knowledge base population and construction of ontological structures. An auxiliary contribution, born from the necessity to evaluate the quality of human subjects, is MACE (Multi-Annotator Competence Estimation), a tool that helps estimate both annotator competence and the most likely answer.2. Learning predicate types (unsupervised and supervised): Relations are ubiquitous in language, and many problems can be modeled as relation problems. We demonstrate this on a common NLP task, word sense disambiguation (WSD) for prepositions (PSD). We use selectional constraints between the preposition and its argument in order to determine the sense of the preposition. In contrast, previous approaches to PSD used n-gram context windows that do not capture the relation structure. We improve supervised state-of-the-art for two type systems.3. Argument types and predicates types (semi-supervised): Previously, there was no work in jointly learning argument and predicate types because (as with many joint learning tasks) there is no jointly annotated data available. Instead, we have two partially annotated data sets, using two disjoint type systems: one with type annotations for the predicates, and one with type annotations for the arguments. We present a semisupervised approach to jointly learn argument types and predicate types, and demonstrate it for jointly solving PSD and supersense-tagging of their arguments. To the best of our knowledge, we are the first to address this joint learning task.Our work opens up interesting avenues for both the typing of existing large collections of triple stores, using all available information, and for WSD of various word classes.
DTEND;TZID=America/Los_Angeles:20130503T160000
DTSTART;TZID=America/Los_Angeles:20130503T150000
LOCATION:11th Floor Large Conference Room [1135]
SUMMARY:Learning Semantic Types and Relations from Text (Defense Practice Talk)
UID:20130503T150000@NL
URL:http://www.isi.edu/natural-language/nl-seminar
END:VEVENT
BEGIN:VEVENT
DESCRIPTION: Narratology analyzes the discursive structure of narratives as finalizedproducts of human invention, such as novels, short-stories, orfairy-tales. Those narratives are rendered in a given surface form;Narratology focuses on narratives in natural language. Narratologistsassume that each narrative surface representation is associated with aneutral, abstract event sequence, the "Story" (histoire, sjuzhet). Theabstractness of Story is illustrated by the fact that the same Story canbe realized in different surface texts. By discursive structure or"Discourse" (discours, fabula), narralogists mean the relation between anabstract Story and its concrete expression in a sequential text. Forexample, if the chronological order of the Story is not respected in itstextual recount, we are dealing with the Discourse parameter of order.Other Discourse parameters include the frequency with which Story eventsare evoked, the point of view from which they are narrated (perceived,evaluated,...), or framed narratives with several narrative levels.The Story Generator Algorithms project at the University of Hamburgevaluated several existing Story Generators with respect to theirdiscursive abilities. It became obvious that most Story Generatorsconcentrate on creating a coherent and chronological abstract Story,which is directly mapped onto natural language. This results in apredominance of 1:1 relations between Story and surface, and in mostcases corresponds to a default or zero instantiation of Discourseparameters. As a consequence, Story Generator outputs tend to be veryexplicit and straightforward, and are likely to be perceived as uniformand boring.Narratological expert knowledge might be useful to future enhanced StoryGenerators and to Natural Language Generation systems dealing withnarrative. One of the aims of Computational Narratology is to model thatexpert knowledge. Ideally, narratological knowledge will be integratedinto a Narratological Structurer, as a processing component of anadvanced system that creates narratives. In such a system, theNarratological Structurer will be the interface between a Story Generatorand subsequent Natural Language Generation modules. The talk alsopresents examples of the knowledge that is being modelled.About the Speaker:Birte Lönneker graduated from the University of Hamburg, Germany, with adegree in French with Finno-Ugristics (Finnish) and BusinessAdministration. Since then, her main fields of publication are CognitiveLinguistics and electronic resources for Natural Language Processing,with special focus on frames and metaphors, as well as electronicdictionaries, corpora, and recently part-of-speech tagging. Her PhD onConcept Frames and Relations, also published as a book in 2003, wasco-supervised at the Institute for Romance Languages and at theDepartment of Informatics in Hamburg. For her Slovenian-German onlinedictionary, Birte Lönneker was twice awarded the EURALEX Laurence UrdangAward. From 2002 to 2004, she received various research grants forSlovenia, where she was working in the Corpus Laboratory of the Instituteof Slovenian Language.Since 2004, Birte Lönneker carries out research on Story GeneratorAlgorithms within the Narratology Research Group Hamburg. She is also aboard member of the German Cognitive Linguistics Association.
DTEND;TZID=America/Los_Angeles:20050620T113000
DTSTART;TZID=America/Los_Angeles:20050620T100000
LOCATION:11 Small
SUMMARY:Between Story Generation and Natural Language Generation
UID:20050620T100000@NL
URL:http://www.isi.edu/natural-language/nl-seminar
END:VEVENT
BEGIN:VEVENT
DESCRIPTION: 11,001 New Features for Statistical Machine Translation (David Chiang)- Winner of Best Paper Award at NAACL/HLT 2009We use the Margin Infused Relaxed Algorithm of Crammer et al. to add alarge number of new features to two machine translation systems: theHiero hierarchical phrase based translation system and oursyntax-based translation system. On a large-scale Chinese-Englishtranslation task, we obtain statistically significant improvements of+1.5 BLEU and +1.1 BLEU, respectively. We analyze the impact of the new features and the performance of the learning algorithm.
DTEND;TZID=America/Los_Angeles:20090515T160000
DTSTART;TZID=America/Los_Angeles:20090515T150000
LOCATION:4th flr CR
SUMMARY:Practice talks for NAACL HLT
UID:20090515T150000@NL
URL:http://www.isi.edu/natural-language/nl-seminar
END:VEVENT
BEGIN:VEVENT
DESCRIPTION: Composing, revising, and editing are highly demanding tasks. Even in polishedand published texts from professional writers we can observe errors and mistakes.For many errors, we can infer how they came to be: Word processors offercharacter-based functions only. These functions do not take into accountelements and structures of the language the author is using. Authors are thusforced to translate their high-level goals into long and complex sequencesof low-level character-based functions. Both the translation process and theexecution of such sequences of functions are error-prone.However, in text editors for programmers ww find so-called language-awareediting functions. These functions operate on the elements and structures of aprogramming or mark-up language and help to avoid errors, as language-awarefunctions make revising and editing less tedious and error-prone.We argue that the concept of language awareness can be transferred to writingnatural language texts using word processors. We propose functions that take thestructures of natural languages into consideration. We distinguish informationfunctions, movement functions, and operations to support revising and editing.The design is based on current findings from writing research.Language-aware editing functions rely on the recognition and categorizationof relevant elements and structures with respect to a certain language. Weuse methods and resources from computational linguistics for morphologicalanalysis and generation, and for part-of-speech tagging. When evaluatingrespective resources we face a rather disappointing situation: NLP resourcesfor German are less suitable than assumed and less applicable for real-worldapplications than usually claimed in the literature.Our prototypical implementation of language-aware functions for revising andediting of German texts serves as a proof of concept. The implementationillustrates opportunities and limits of current NLP resources for German.
DTEND;TZID=America/Los_Angeles:20110916T160000
DTSTART;TZID=America/Los_Angeles:20110916T150000
LOCATION:11th Floor Large Conference Room [1135]
SUMMARY:Linguistically supported editing and revising: concept and prototypical implementation based on interactive NLP resources
UID:20110916T150000@NL
URL:http://www.isi.edu/natural-language/nl-seminar
END:VEVENT
BEGIN:VEVENT
DESCRIPTION: 3:30pm Mark Hopkins (UCLA)Tree Sequence Automata: A Unifying Framework for Tree Relation FormalismsThere exist a wide variety of competing formalisms for representing alanguage of ordered tree pairs. These include (bottom-up and top-down)tree transducers, synchronous tree-substitution grammars (STSGs),synchronous tree-adjoining grammars (STAGs), and inversion transductiongrammars (ITGs). Since these formalisms have all developed independentlyof one another, it is difficult to compare their respectiverepresentational power. This work seeks to make this task simpler byviewing these formalisms as instances of a general unifying formalism,which we call tree sequence automata (TSA). By casting these differentformalisms in a single framework, we can compare them directly by studyingthe specific subclass of TSA that they fall into.4:00pm Jason Riesa (Johns Hopkins)A case study in building a cost-effective speech-to-speech machine translation system with sparse resources: English - Iraqi ArabicThe Arabic spoken dialect of Iraq is a language deprived of the vastresources that researchers enjoy when working with its writtencounterpart, Modern Standard Arabic (MSA). The Iraqi Arabic lexicon andgrammar are also sufficiently distinct so that the use of existing toolsor corpora for MSA yield little or no positive effect on machinetranslation output quality. One can see that building a machinetranslation system normally dependent on a large parallel corpus is aparticularly difficult task when given just a 37,000 line translatedparallel text based on transcribed speech. This talk will explore theconstraints involved in working with this type of data, how we endeavoredto mitigate such problems as a non-standard orthography and a highlyinflected grammar, and propose a cost- effective way for dealing with suchprojects in the future.4:30pm Preslav Nakov (UC Berkeley)Multilingual Word AlignmentRecently there has been a growing number of available multilingualparallel texts. One such source is the European Union, which publishes itsofficial documents in the official languages of all member states(sometimes also in the languages of the candidates). Another source arethe United Nations. These corpora are a great source of training data formachine translation between new language pairs. But they also offer theopportunity to obtain better pairwise word alignments by looking atmultiple languages in parallel. In this talk I will present my research asa summer intern at ISI on getting better French (Fr) to English (En) wordalignments using an additional language (Xx). First, I will introduce twoheuristics which start with pairwise alignments between Fr-Xx, En-Xx andFr-En and then combine them probabilistically (in a linear model) orgraph-theoretically (by looking at in- and out-degrees for each word).Then I will present two Model1 inspired alignment models: (a) from "Fr andXx" to En; and (b) from Fr to "En and Xx".
DTEND;TZID=America/Los_Angeles:20050824T170000
DTSTART;TZID=America/Los_Angeles:20050824T153000
LOCATION:11 Large
SUMMARY:Summer Student Presentations
UID:20050824T153000@NL
URL:http://www.isi.edu/natural-language/nl-seminar
END:VEVENT
BEGIN:VEVENT
DESCRIPTION: Models that align phrases instead of words offer anappealing alternative to the standard relative frequency estimates ofphrase translation probabilities. But, while some effective wordalignment models (Model 1, Model 2 & HMM) can be estimated tractablywith EM, phrase alignment models cannot. I'll talk about how to showthat estimation and inference under these models is intractable.Then, I'll present two useful approximation techniques.First, I'll talk about how to cast phrase alignment search as aninteger linear programming (ILP) problem and find the optimalalignment reliably and quickly with off-the-shelf ILP software. Someapplications of this technique include training phrase alignmentmodels and interpreting the output of word alignment models.Second, we'll look at how to estimate translation probabilities undera phrase alignment model using a Gibbs sampling procedure. Thesampler has some nice asymptotic convergence properties and also seemsto produce good results in practice. I'll walk through the differentmodels we've trained and how they performed.Time permitting, I'll also talk about some of the ways in which wecould potentially extend this work to syntactic MT.
DTEND;TZID=America/Los_Angeles:20080509T160000
DTSTART;TZID=America/Los_Angeles:20080509T150000
LOCATION:11 Large
SUMMARY:Inference in phrase alignment models
UID:20080509T150000@NL
URL:http://www.isi.edu/natural-language/nl-seminar
END:VEVENT
BEGIN:VEVENT
DESCRIPTION: Two decades after their invention, the IBM word-based translation models, widely available in the GIZA++ toolkit, remain the dominant approach to word alignment and an integral part of many statistical translation systems. Although many models have surpassed them in accuracy, none have supplanted them in practice.We propose a simple extension to the IBM models: an l0 prior to encourage sparsity in the word-to-word translation model. This extension has been implemented in GIZA++ and scales to large-scale data . We achieve significant improvements over IBM Model 4 in both word alignment and translation quality.This is a practice talk for ACL.Bio:Ashish Vaswani is a PhD student at ISI.
DTEND;TZID=America/Los_Angeles:20120703T160000
DTSTART;TZID=America/Los_Angeles:20120703T150000
LOCATION:4th Floor Conference Room
SUMMARY:Smaller Alignment Models for Better Translations: Unsupervised Word Alignment with the l0-norm
UID:20120703T150000@NL
URL:http://www.isi.edu/natural-language/nl-seminar
END:VEVENT
BEGIN:VEVENT
DESCRIPTION: TBA
DTEND;TZID=America/Los_Angeles:20050218T163000
DTSTART;TZID=America/Los_Angeles:20050218T150000
LOCATION:11 Large
SUMMARY:TBA
UID:20050218T150000@NL
URL:http://www.isi.edu/natural-language/nl-seminar
END:VEVENT
BEGIN:VEVENT
DESCRIPTION: This talk will be a continuation of topics from Monday's talk.
DTEND;TZID=America/Los_Angeles:20100610T170000
DTSTART;TZID=America/Los_Angeles:20100610T160000
LOCATION:10th Floor Conference Room
SUMMARY:"Bayesian models of language acquisition" or "Where do the rules come from?" (continued from 7 Jun 2010)
UID:20100610T160000@NL
URL:http://www.isi.edu/natural-language/nl-seminar
END:VEVENT
BEGIN:VEVENT
DESCRIPTION: Due to the availability of large amounts of training data andcomputational resources, building more complex models with sentencelevel knowledge and longer dependencies has been an active area ofresearch in automatic speech recognition (ASR). Yet, due to thecomplexity of the speech recognition task, integration of many ofthese complex and sophisticated knowledge sources into the firstdecoding pass is not feasible. Many of these long-span models cannotbe represented as weighted finite-state automata (WFSA), making itdifficult even to incorporate them in a lattice rescoring pass.First, we motivate our work by providing compelling empirical evidencethat n-gram LMs are not sufficient for ASR task and why we need toincorporate non-local features such as syntax. The development oflanguage models with such long-span (non-local) features is underway,but is not addressed in this talk. We instead address how such modelsshould be trained discriminatively and applied effectively.Specifically, we describe a new approach for rescoring speech latticeswith such models (acoustic or language) that does not entailcomputationally intensive lattice expansion or limited rescoring ofonly an N -best list.We view the set of word-sequences in a lattice as a discrete space anddevelop a hill climbing technique to start with, say, the 1-besthypothesis under the lattice-generating model(s) and iterativelyimprove it using the new model. We demonstrate empirically that toachieve the same reduction in error rate using a better estimated,higher order LM, our technique evaluates fewer hypotheses thanconventional N-best rescoring by up to two orders of magnitude.We also propose to integrate the idea of hill climbing into thetraining of discriminative language models with non-local sentencelevel features. Discriminative models provide the flexibility toinclude both local n-gram features and arbitrary sentence levelfeatures. However, unlike generative LMs with long-span dependencieswhere one has to resort to N-best lists only during decoding(rescoring), discriminative models force the use of N-best lists evenfor LM training. We demonstrate significant computational saving during training as well as error-rate reduction over N-best training methods.Bio:Ariya Rastrow is a Ph.D. candidate at Johns Hopkins University,working with Sanjeev Khudanpur and Mark Dredze. He was initiallyadvised by Fred Jelinek. The focus of his PhD research is to advancespeech recognition systems to efficiently incorporate linguisticallymotivated non-local features into language models. In his recent work,he has developed an efficient hill-climbing algorithm to applynon-local complex models for the speech recognition task. He has alsoworked on out-of-vocabulary (OOV) detection, spoken term detection andsemi-supervised adaptation techniques for speech recognition.
DTEND;TZID=America/Los_Angeles:20111104T160000
DTSTART;TZID=America/Los_Angeles:20111104T150000
LOCATION:11th Floor Large Conference Room [1135]
SUMMARY:Going beyond n-grams: Incorporating non-local dependencies for Speech Recognition
UID:20111104T150000@NL
URL:http://www.isi.edu/natural-language/nl-seminar
END:VEVENT
BEGIN:VEVENT
DESCRIPTION: (This is a practice talk for a paper by Giorgio Satta and Enoch Peserico)This paper investigates some computational problems associated withprobabilistic translation models that have recently been adopted in theliterature on machine translation. These models can be viewed as pairs ofprobabilistic context-free grammars working in a `synchronous' way. Twohardness results for the class NP are reported, along with an exponentialtime lower-bound for certain classes of algorithms that are currently usedin the literature.
DTEND;TZID=America/Los_Angeles:20050930T163000
DTSTART;TZID=America/Los_Angeles:20050930T150000
LOCATION:4 Large
SUMMARY:Some Computational Complexity Results for Synchronous Context-Free Grammars
UID:20050930T150000@NL
URL:http://www.isi.edu/natural-language/nl-seminar
END:VEVENT
BEGIN:VEVENT
DESCRIPTION: This dissertation studies how people describe emotions with languageand how computers can simulate this descriptive behavior. Althoughmany non-human animals can express their current emotions as socialsignals, only humans can communicate about emotions symbolically.This symbolic communication of emotion allows us to talk aboutemotions that we may not currently be feeling, for example describingemotions that occurred in the past, gossiping about the emotions ofothers, and reasoning about emotions hypothetically. Another feature of thisdescriptive behavior is that we talk about emotions as if they werediscrete entities, even though we may not always have necessary andsufficient observational cues to distinguish one emotion from another,or even to say what is and is not an emotion. This motivates us tofocus on aspects of meaning that are learned primarily throughlanguage interaction rather than by observations through the senses.To capture these intuitions about how people describe emotions, wepropose the following thesis: natural language descriptions of emotionare definite descriptions that refer to intersubjective theoretical entities.We support our thesis using theoretical, experimental, computationalresults. The theoretical arguments use Russell's notion of definitedescriptions, Carnap's notion of theoretical entities, and thequestion-asking period in child language acquisition. The experimentaldata we collected include dialogs between humans and computers andweb-based surveys, both using crowd-sourcing on Amazon MechanicalTurk. The computational models include a dialog agent based onsequential Bayesian belief update within a generalized pushdown automaton,as well as a fuzzy logic model of similarity and subsethood between emotion terms.For future work, we propose a research agenda that includes acontinuation of work on the emotion domain as well as new work onother domains where subjective descriptions are established throughnatural language communication.Short Bio:Abe Kazemzadeh is a PhD candidate at the USC Computer Science Dept anda research assistant at the the Signal Analysis and InterpretationLaboratory (SAIL). His interests include natural language, logic,emotions, games, and algebra. He is currently the chief technologyofficer at the USC Annenberg Innovation Laboratory (AIL).
DTEND;TZID=America/Los_Angeles:20130111T160000
DTSTART;TZID=America/Los_Angeles:20130111T150000
LOCATION:11th Floor Large Conference Room [1135]
SUMMARY:Natural Language Description of Emotion (Ph.D. Thesis Defense Practice Talk)
UID:20130111T150000@NL
URL:http://www.isi.edu/natural-language/nl-seminar
END:VEVENT
BEGIN:VEVENT
DESCRIPTION: I'll give a survey of trees and grammars, at least the parts that seemmost relevant to ongoing work at ISI. This will be a theory talk. I'llstart with context-free grammars, which were developed in the 1950s, andcover other tree-generating systems. I'll also talk abouttree-transforming systems.
DTEND;TZID=America/Los_Angeles:20040709T163000
DTSTART;TZID=America/Los_Angeles:20040709T150000
LOCATION:11 Large
SUMMARY:Survey of Trees and Grammars
UID:20040709T150000@NL
URL:http://www.isi.edu/natural-language/nl-seminar
END:VEVENT
BEGIN:VEVENT
DESCRIPTION: Bake-offs, shared tasks, evaluations: these are names for short,high-stress periods in many CS researchers' lives where theiralgorithms and models are exposed to unseen data, often withreputations and funding on the line. Evaluations are sometimesperceived to be the bane of much of our work lives. Wegrouse about metrics, procedures, glitches, and all thetime "wasted" chasing scores, rather than doing RealScience (TM). In this talk I will argue that despite valid criticismsof the approach, coordinated evaluation is a net benefit to NLPresearch and has led to accomplishments that might not have otherwisearisen. This argument will frame a more in-depth discussion of severalpieces of recent evaluation-grounded work: rapid generation oftranslation and information extraction for low-resource surpriselanguages (DARPA LORELEI) and organization of SemEval sharedtasks in semantic parsing and generation.Jonathan May is a Research Assistant Professor at the University ofSouthern California's Information Sciences Institute(USC/ISI). Previously, he was a research scientist at SDL Research(formerly Language Weaver) and a scientist at Raytheon BBNTechnologies. He received a Ph.D. in Computer Science from theUniversity of Southern California in 2010 and a BSE and MSE inComputer Science Engineering and Computer and Information Science,respectively, from the University of Pennsylvania in 2001. Jon'sresearch interests include automata theory, natural languageprocessing, machine translation, and machine learning.
DTEND;TZID=America/Los_Angeles:20170120T160000
DTSTART;TZID=America/Los_Angeles:20170120T150000
LOCATION:6th Floor Large Conference Room [689]
SUMMARY:How I Learned to Stop Worrying and Love Evaluations (and Keep Worrying)
UID:20170120T150000@NL
URL:http://www.isi.edu/natural-language/nl-seminar
END:VEVENT
BEGIN:VEVENT
DESCRIPTION: Natural language is riddled with many ambiguities, greatly complicatingnatural language processing tasks. Current parsers reconstruct thesyntax of sentences without addressing the numerous ambiguities oflanguage. This talk discusses a proposed solution forsemantically-enriched parsing that consists of ontological resources,datasets, and tools that can be used to produce more informative parsesof English sentences. The resulting parses consist not only of syntacticstructure, but also semantic interpretations for noun compounds,preposition senses, and possessive constructions.
DTEND;TZID=America/Los_Angeles:20101112T160000
DTSTART;TZID=America/Los_Angeles:20101112T150000
LOCATION:11th Floor Large Conference Room [1135]
SUMMARY:Semantically-enriched Parsing for Natural Language Understanding (Ph.D. Proposal practice talk)
UID:20101112T150000@NL
URL:http://www.isi.edu/natural-language/nl-seminar
END:VEVENT
BEGIN:VEVENT
DESCRIPTION: There is abundant knowledge out there carried in the form of natural language texts, such as social media posts, scientific research literature, medical records, etc., which grows at an astonishing rate. Yet this knowledge is mostly inaccessible to computers and overwhelming for human experts to absorb. Information extraction (IE) processes raw texts to produce machine understandable structured information, thus dramatically increasing the accessibility of knowledge through search engines, interactive AI agents, and medical research tools. However, traditional IE systems assume abundant human annotations for training high quality machine learning models, which is impractical when trying to deploy IE systems to a broad range of domains, settings and languages. In this talk, I will present how to leverage the distributional statistics of characters and words, the annotations for other tasks and other domains, and the linguistics and problem structures, to combat the problem of inadequate supervision, and conduct information extraction with scarce human annotations.Nanyun Peng is a PhD candidate in the Department of Computer Science at Johns Hopkins University, affiliated with the Center for Language and Speech Processing and advised by Dr. Mark Dredze. She is broadly interested in Natural Language Processing, Machine Learning, and Information Extraction. Her research focuses on using deep learning for information extraction with scarce human annotations. Nanyun is the recipient of the Johns Hopkins University 2016 Fred Jelinek Fellowship. She has completed two research internships at IBM T.J. Watson Research Center, and Microsoft Research Redmond. She holds a master's degree in Computer Science and BAs in Computational Linguistics and Economics, all from Peking University.
DTEND;TZID=America/Los_Angeles:20170223T160000
DTSTART;TZID=America/Los_Angeles:20170223T150000
LOCATION:11th Floor Large Conference Room [1135]
SUMMARY:Representation Learning with Joint Models for Information Extraction
UID:20170223T150000@NL
URL:http://www.isi.edu/natural-language/nl-seminar
END:VEVENT
BEGIN:VEVENT
DESCRIPTION: Most state-of-the-art techniques used in natural language processing (NLP) are supervised and require labeled training data. For example, statistical language translation requires huge amounts of bilingual data for training translation systems. But such data does not exist for all language pairs and domains. Using human annotation to create new bilingual resources is not a scalable solution. This raises a key research challenge: How can we circumvent the problem of limited labeled resources for NLP applications? Interestingly, cryptanalysts and archaeologists have tackled similar challenges in solving "decipherment problems".This thesis work aims to bring together techniques from classical cryptography, NLP and machine learning. We introduce a novel approach called "natural language decipherment" that can solve natural language problems without labeled (parallel) data. In this talk, we show how a wide variety of NLP problems can be formulated as decipherment tasks---for example, in statistical language translation one can view the foreign-language text as a cipher for English. Instead of relying on parallel training data, decipherment uses knowledge of the target language (e.g., English) and large quantities of readily available monolingual source (cipher) data to induce bilingual connections between the source and target languages. Using decipherment techniques, we make headway in attacking a hierarchy of problems ranging from letter substitution decipherment to sequence labeling problems (such as part-of-speech tagging) to language translation. Along the way, we make several key contributions---novel unsupervised algorithms that search for minimized models during decipherment and achieve state-of-the-art results on a number of important natural language tasks. Unlike conventional approaches, these decipherment methods can be easily extended to multiple domains and languages (especially resource-poor languages), thereby helping to spread the impact and benefits of NLP research.
DTEND;TZID=America/Los_Angeles:20110318T160000
DTSTART;TZID=America/Los_Angeles:20110318T150000
LOCATION:11th Floor Large Conference Room [1135]
SUMMARY:Deciphering Natural Language
UID:20110318T150000@NL
URL:http://www.isi.edu/natural-language/nl-seminar
END:VEVENT
BEGIN:VEVENT
DESCRIPTION: Abstract: Computational creativity is an emerging field of AI, with linguistic creativity being an interesting test-bed for developing and evaluating machines with reasoning capabilities. A concrete example is story generation and understanding, a task which unlike the vast majority of traditional NLP that treats sentences in isolation, requires deep understanding of the general context and discourse of stories.In this talk, I will present some preliminary steps towards this goal and show how sequence-to-sequence models can be applied to this task. Overall, our results on story understanding are on par with current state-of-the-art (that nevertheless have no generative capabilities), while at the same time producing sometimes rather amusing story endings.Bio: Angeliki is a final year PhD student at the Center for Mind/Brain Sciences of the University of Trento. She received her MSc from the Saarland University, where she worked with Ivan Titov and Caroline Sporleder on Bayesian models for sentiment and discourse. She is currently working at the intersection between language and vision under the supervision of Marco Baroni.Webcast: http://webcastermshd.isi.edu/Mediasite/Play/6f51b67c1a304a0c83297dd2f9b453921d
DTEND;TZID=America/Los_Angeles:20160803T115900
DTSTART;TZID=America/Los_Angeles:20160803T110000
LOCATION:11th Floor Large Conference Room [1135]
SUMMARY:Can machines understand and generate stories?
UID:20160803T110000@NL
URL:http://www.isi.edu/natural-language/nl-seminar
END:VEVENT
BEGIN:VEVENT
DESCRIPTION: (Yarowsky et al., 2001) present an algorithm for bootstrapping a POStagger for an arbitrary target language, using an existing POS tagger fora source language and a parallel corpus in the source and targetlanguages. The source text is annotated with the POS tagger; the parallelcorpus is word-aligned; the POS tags are "projected" from source to targetlanguage; and finally smoothing is performed before training a POS taggerfor the target language on the projected annotations.I will talk about my work (jointly with my advisor, Steve Abney, at U. ofMichigan) in which we extend this algorithm by projecting from multiplesource languages onto a target language, then combining the outputs tocompute a consensus POS tagger. Our hypothesis is that systematictransfer errors from different source-target pairs can be reduced by usingmultiple source languages. I will present experimental results for threedifferent source languages (English, German, and Spanish), and twodifferent target languages (French and Czech). Our results indicate thatusing multiple source languages improves performance.
DTEND;TZID=America/Los_Angeles:20050715T163000
DTSTART;TZID=America/Los_Angeles:20050715T150000
LOCATION:11 Large
SUMMARY:Inducing POS Taggers by Projecting from Multiple Source Languages
UID:20050715T150000@NL
URL:http://www.isi.edu/natural-language/nl-seminar
END:VEVENT
BEGIN:VEVENT
DESCRIPTION: How are concepts represented in the brain? When we hear the ringing of a bell, or watch a bell swinging back and forth, is there a shared "BELL" pattern of neural activity in our brains? Philosophers have debated the nature of concepts for centuries, but recent technical advances have allowed neuroscientists to make contributions to this topic. The combination of functional neuroimaging and machine learning has allowed us to examine distributed patterns of activity in the human brain to decode what they represent about the world, and to what level of abstraction. I describe our recent findings that revealed a hierarchical organization of multisensory information integration, leading to representations that generalize across different sensory modalities. I will also discuss our work on the social function of concepts, which enables the communication of similar thoughts and associations between individuals.Bio:I am a research associate at the Brain and Creativity Institute of the University of Southern California. I earned my Ph.D. at USC, mentored by Antonio Damasio. I am interested in the general problem of consciousness, and in particular how different sensations are bound together by the brain into a unified experience of the world.
DTEND;TZID=America/Los_Angeles:20141205T160000
DTSTART;TZID=America/Los_Angeles:20141205T150000
LOCATION:6th Floor Large Conference Room [689]
SUMMARY:Multisensory integration in a neural framework for concepts
UID:20141205T150000@NL
URL:http://www.isi.edu/natural-language/nl-seminar
END:VEVENT
BEGIN:VEVENT
DESCRIPTION: Lexical cohesion refers to structure created in a text by use of words withrelated meanings. Apart from its importance in theoretical and appliedlinguistics, lexical cohesion detection is used in NLP tasks like topicsegmentation, extractive summarization, spelling correction, etc. However, theintuitive potential of lexical cohesion for such tasks is often not realized inpractice, possibly due to shortcomings of detection algorithms.I will briefly describe an experiment with readers aimed at providing reliabledata for a computational investigation of lexical cohesion. We then discuss anumber of informative features for cohesion detection, drawing on sources likeWordNet, distributional information, free associations, and the structure ofinformation in the text itself. Finally, I report experimentswith supervised learning of lexical cohesion.About the speaker:Beata Beigman Klebanov is a PhD candidate at the Hebrew University of Jerusalem,Israel, currently a visiting scholar at Northwestern University. Beata'sinterests are in experimental, computational and applied research in textpragmatics.
DTEND;TZID=America/Los_Angeles:20070105T163000
DTSTART;TZID=America/Los_Angeles:20070105T150000
LOCATION:11 Large
SUMMARY:Experimental and Computational Investigation of Lexical Cohesion in Texts
UID:20070105T150000@NL
URL:http://www.isi.edu/natural-language/nl-seminar
END:VEVENT
BEGIN:VEVENT
DESCRIPTION: The Arabic language exhibits diglossia, i.e., the coexistence of two formsof language, a variety with standard orthography and sociopolitical cloutwhich is not natively spoken by anyone (Modern Standard Arabic, MSA) andvarieties that are primarily spoken and lack writing standards (Arabicdialects). There are important resources currently available for MSA withmuch on-going NLP work; for example, there is an Arabic Treebank andseveral syntactic parsers for MSA. However, Arabic dialect resources andNLP research are still at an infancy stage. I will present work done atthe Johns Hopkins CLSP Summer Workshop on parsing of Arabic dialects, inparticular, Levantine Arabic. We have experimented with three approachesto leveraging MSA resources to create a parser for Levantine Arabic, aswell as methods for induction of MSA-Levantine translation lexicons and aLevantine part-of-speech tagger. Using these methods we obtain errorreductions of up to 15% compared with applying an MSA parser directly toLevantine text.Rambow et al. Parsing Arabic Dialects: Final Report. Johns HopkinsUniversity Center for Language and Speech Processing Workshop 2005.http://www.clsp.jhu.edu/ws2005/groups/arabic/documents/finalreport.pdfChiang et al. Parsing Arabic Dialects. To appear in Proc. EACL 2006.This is joint work with O. Rambow, M. Diab, N. Habash, R. Hwa, K. Sima'an,V. Lacey, R. Levy, C. Nichols and S. Shareef.
DTEND;TZID=America/Los_Angeles:20060210T160000
DTSTART;TZID=America/Los_Angeles:20060210T150000
LOCATION:11 Large
SUMMARY:Parsing Arabic Dialects
UID:20060210T150000@NL
URL:http://www.isi.edu/natural-language/nl-seminar
END:VEVENT
BEGIN:VEVENT
DESCRIPTION: We show that phrase structures in Penn Treebank style parsesare not optimal for syntax-based machine translation. Weexploit a series of binarization methods to restructure thePeen Treebank style trees such that syntactified phrasessmaller than Penn Treebank constituents can be acquired andexploited in translation. We find that by employing the EMalgorithm for determining the binarization of a parse treeamong a set of alternative binarizations gives us the besttranslation result.
DTEND;TZID=America/Los_Angeles:20070525T153000
DTSTART;TZID=America/Los_Angeles:20070525T150000
LOCATION:11 Large
SUMMARY:Binarizing Syntax Trees to Improve Syntax-Based Machine Translation Accuracy
UID:20070525T150000@NL
URL:http://www.isi.edu/natural-language/nl-seminar
END:VEVENT
BEGIN:VEVENT
DESCRIPTION:
DTEND;TZID=America/Los_Angeles:20030801T160000
DTSTART;TZID=America/Los_Angeles:20030801T150000
LOCATION:11 Large
SUMMARY:Toward deciphering the 2-dimensional ancient Luwian script by discovering its writing order
UID:20030801T150000@NL
URL:http://www.isi.edu/natural-language/nl-seminar
END:VEVENT
BEGIN:VEVENT
DESCRIPTION: The modeling of discourse has been a major topic of research in the linguistics and AI communities for decades. With respect to language, discourse phenomena refer to the use of linguistic indicators that reflect the functional organization of utterances, relationships between different utterances, with the interlocutors' state of mind and with the situational surrounding.The development of models of discourse that are operationalizable (as a part of NLP applications) is essential, for example, in machine translation:* to interpret, to translate and to generate pronouns, definite and indefinite NPs correctly,* to translate non-canonical constructions (e.g., passive),* to generate the correct word order (e.g., when translating into a free-word order language),* to insert or to drop discourse markers and conjunctions, or* to choose the appropriate type of syntactic embedding in complex sentences.In other branches of NLP, different aspects of discourse are important, e.g., relations between utterances (machine reading), the hierarchical organization of discourse (text summarization) and the sequential organization of utterances in a text (text structuring/natural language generation).Numerous models of different aspects of discourse have been proposed, including discourse structure (the hierarchical organization of utterances in discourse), discourse relations (relations between independent utterances in discourse), information structure (the functional structure of utterances in context), and information status (accessibility of antecedents of pronouns, definite descriptions and elliptic constructions). These approaches range from relatively abstract models from cognitive and functional linguistics (e.g., Givon 1983), over elaborate formal models developed in formal semantics (e.g., Asher 1993), to "parameterized", rule-based models in AI (e.g., Grosz et al. 1995).Since the mid-1990s, this traditional, "theory-centered" line of research has been complemented with an "annotation-centered" methodology, i.e., the development and the use of annotated corpora to test predictions and to develop statistical classifiers. In the first part of the talk, I describe selected activities of the applied computational linguistics group at the University of Potsdam/Germany in this direction, which include* the annotation of discourse structure, coreference, information structure and information status (Stede 2004, Krasavina and Chiarcos 2007, Ritz et al. 2008)* the development of generic multi-layer architectures capable to represent and to access these annotations along with other types of annotation applied to the same stretch of data (Chiarcos et al. 2008), e.g., annotations for constituent syntax, dependency syntax, or frame semantics, and* the application of machine learning techniques to predict discourse features from less abstract annotation layers (Ritz 2007, Chiarcos 2011).The primary drawback of annotation-centered models are the immense cognitive (and thus, financial) efforts necessary to produce reliable discourse annotations. One way to address this problem is to make use of corpora without discourse annotations to test predictions of candidate models, and to develop unsupervised or weakly supervised approaches to support or to replace manual annotation.In the second part of my talk, this "data-centered" approach on discourse will be illustrated for the example of discourse relations, one of the main topics of my work at ISI. I describe a pilot study that shows that significant, reproducible and interpretable insights about the discourse relation (that is likely to be) connecting a pair of events can be achieved from a sufficiently large corpus with syntax annotations only. Further, possible lines for subsequent research will be sketched.Nicholas Asher (1993). Reference to Abstract Objects in Discourse. Kluwer, Dordrecht, 1993.Christian Chiarcos (2011). Evaluating salience metrics for the context-adequate realization of discourse referents. In: Proceedings of the 13th European Workshop on Natural Language Generation (ENLG 2011). Association of Computational Linguistics, Nancy, France, Sep 2011, 32-43.Christian Chiarcos, Stefanie Dipper, Michael Gotze, Ulf Leser, Anke Lüdeling, Julia Ritz, and Manfred Stede (2008). A Flexible Framework for Integrating Annotations from Different Tools and Tagsets. TAL (Traitement automatique des langues) 49 (2): 218-248.Talmy Givon (ed., 1983). Topic Continuity in Discourse: A Quantitative Cross-Language Study. John Benjamins, Amsterdam and Philadelphia.Barbara J. Grosz, Aravind K. Joshi, and Scott Weinstein (1995). Centering: A framework for modelling the local coherence of discourse. Computational Linguistics, 21(2):203â225.Olga Krasavina and Christian Chiarcos (2007). PoCoS - Potsdam Coreference Scheme. In Proceedings of the Linguistic Annotation Workshop. Held in Conjunction with the ACL-2007, Prague, Czech Republic, pages 156â163.Julia Ritz, Svetlana Petrova, Michael Götze, and Stefanie Dipper (2007). Automatic Identification of Information Structure in Small Corpora of Modern and Old High German. GLDV-Fruhjahrstagung 2007, Tubingen, Germany.Julia Ritz, Stefanie Dipper, und Michael Götze (2008). Annotation of Information Structure: An Evaluation Across Different Types of Texts. In Proceedings of the the 6th LREC conference. Marrakech, Morocco.Manfred Stede (2004). The Potsdam Commentary Corpus. In Bonnie Webber and Donna K. Byron, editors, Proceedings of the ACL-2004 Workshop on Discourse Annotation, Barcelona, pages 96â102.Biography:Christian Chiarcos, born 1977, studied Computer Science (MSc, 2002) and General Linguistics (MA, 2004) at the Technical University Berlin, Germany. From 2002 to 2003, he received a scholarship in the context of the project "Collocations in Dictionary" at the Berlin-Brandenburg Academy of Science under the auspicion of Christiane Fellbaum (Princeton). From 2003 to 2005, he participated in the graduate school "Economy and Complexity in Language" at the Humboldt-Unversity at Berlin and the University of Potsdam, Germany, where he developed a corpus-based approach to predict syntactic alternations for Natural Language Generation. This research formed the basis for his PhD thesis "Mental Salience and Grammatical Form" (University of Potsdam, 2010).Since 2006, he worked in the Applied Computational Linguistics group at the University of Potsdam, Germany, where he participated in different research projects dedicated to the development of interoperable infrastructures for NLP and multi-layer corpora. Since 2007, this research was carried out in the context of the Collaborative Research Center "Information Structure", a multidisciplinary network of projects at the University of Potsdam and the Humboldt-University Berlin, dedicated to the study of discourse phenomena.
DTEND;TZID=America/Los_Angeles:20120427T160000
DTSTART;TZID=America/Los_Angeles:20120427T150000
LOCATION:11th Floor Large Conference Room [1135]
SUMMARY:Towards operationalizable models of discourse phenomena: Addressing discourse relations
UID:20120427T150000@NL
URL:http://www.isi.edu/natural-language/nl-seminar
END:VEVENT
BEGIN:VEVENT
DESCRIPTION: I'll talk about some unsupervised learning experiments -- how I was satisfied with the initial results, how I became very dissatisfied, and how I became (somewhat) satisified again.
DTEND;TZID=America/Los_Angeles:20080111T163000
DTSTART;TZID=America/Los_Angeles:20080111T150000
LOCATION:11 Large
SUMMARY:How to Make EM Do What You Want
UID:20080111T150000@NL
URL:http://www.isi.edu/natural-language/nl-seminar
END:VEVENT
BEGIN:VEVENT
DESCRIPTION: We discuss preliminary work on a possible approach to exploitingsyntax in an effective way for machine translation. The drivingguideline is to devise a machine translation system that can performeffectively, given a very limited quantity of parsed training data.
DTEND;TZID=America/Los_Angeles:20061127T163000
DTSTART;TZID=America/Los_Angeles:20061127T150000
LOCATION:11 Large
SUMMARY:Towards the Effective Exploitation of Syntax in Machine Translation
UID:20061127T150000@NL
URL:http://www.isi.edu/natural-language/nl-seminar
END:VEVENT
BEGIN:VEVENT
DESCRIPTION: For a large number of natural language processing (NLP) problems, we are concerned with finding semantic patterns from input sequences. In recurrent neural network (RNN) based approach, such pattern is âencodedâ in a vector called hidden state. Since Elmanâs âFinding structure in timeâ published in 1990, it has long been believed that the âmagic powerâ of RNNâs memory, which is enclosed inside the hidden state, can handle very long sequences. Yet besides some experimental observations, there is no formal definition of RNNâs memory, let alone a rigid mathematical analysis of how RNNâs memory forms.This talk will focus on understanding memory from two viewpoints. The first viewpoint is that memory is a function that maps certain elements in the input sequences to the current output. Such definition, for the first time in literature, allows us to do detailed analysis of the memory of simple RNN (SRN), long short-term memory (ELSTM), and gated recurrent unit (GRU). It also opens the door for further improving the existing RNN basic models. The end results are the proposal of a new basic RNN model called extended LSTM (ELSTM) with outstanding performance for complex language tasks, and a new macro RNN model called dependent bidirectional RNN (DBRNN) with smaller cross entropy than bidirectional RNN (BRNN) and encoder-decoder (enc-dec) models.The second viewpoint is that memory is a compact representation of sparse sequential data. From this perspective, the process of generating hidden state of RNN is simply dimension reduction. Thus, method like principal component analysis (PCA) which does not require labels for training becomes attractive. However, there are two known problems in implementing PCA for NLP problems: the first is computational complexity; the second is vectorization of sentence data for PCA. To deal with this problem, an efficient dimension reduction algorithm called tree -structured multi-linear PCA is proposed.Bio: Yuanhang Su received the dual B.S. degree in Electrical Engineering & Automation and Electronic & Electrical Engineering from University of Strathclyde, Glasgow, U.K. and Shanghai University of Electric Power, Shanghai, China, respectively in 2009, and the M.S. degree in Electrical Engineering from the University of Southern California, Los Angeles, CA, in 2010. From 2011 to 2015, he worked as image/video/camera software and algorithm engineer for a Los Angeles startup named Exaimage, Shanghai Aerospace Electronics Technology Institute in China and Huawei Technology in China consecutively. He joined MCL lab in 2016 spring, and is currently pursing his Ph.D. in computer vision, natural language processing and machine learning.
DTEND;TZID=America/Los_Angeles:20180413T160000
DTSTART;TZID=America/Los_Angeles:20180413T150000
LOCATION:11th Floor Large Conference Room [1135]
SUMMARY:Finding memory in time
UID:20180413T150000@NL
URL:http://www.isi.edu/natural-language/nl-seminar
END:VEVENT
BEGIN:VEVENT
DESCRIPTION: Randomized data structures can help us scale discrete models encountered in NLP. This talk will describe their use in language modeling and present some more general related results.N-gram language models are fundamental to speech recognition and machine translation. Unfortunately, the n-gram parameter space grows exponentially with the dimension of the feature vector. I will describe how randomization can be used to remove the space-dependency of such models on the a priori parameter space.The novel extensions of the Bloom filter that I will present are able to take advantage of the entropy of the distribution of values assigned to feature vectors to save space in a discrete statistical model. I will review some results applying these models to language modeling in machine translation and relate their space-requirements to a novel lower bound on the general problem of querying a map of key/value pairs.No prior knowledge of randomized data structures will be assumed.
DTEND;TZID=America/Los_Angeles:20071012T163000
DTSTART;TZID=America/Los_Angeles:20071012T150000
LOCATION:11 Large
SUMMARY:Scalable Language Modeling: Breaking the Curse of Dimensionality
UID:20071012T150000@NL
URL:http://www.isi.edu/natural-language/nl-seminar
END:VEVENT
BEGIN:VEVENT
DESCRIPTION: As part of an effort to encode the commonsense knowledge we need innatural language understanding, I have been looking at several very commonwords and their uses in diverse corpora, and asking what we have to knowto understand this word in this context. In this talk, I will describethe investigations of the uses of two words -- the adverb "now" and thepreposition "like".One might think that "now" simply expresses a temporal property of anevent. But in fact in almost every instance, it is used to point up acontrast -- "This is true now. Something else was true then." It is thusmore of a relation than a property. I will describe several categories ofsuch relations. Another question of interest about "now" is "How long aperiod is the word "now" describing in its various uses?": "I'm typing anabstract now" vs. "We travel by automobile now." I suggest somecategories of knowledge that need to be encoded to answer this question.When we successfully understand "A is like B", we have figured out someproperty that A and B have in common. How can we find that propertycomputationally? In the data I looked at, in 80% of the instances, theproperty is explicit in the nearby text, and I will talk about how we canidentify it. For the remainder I examine the knowledge we would need inorder to infer the common property.
DTEND;TZID=America/Los_Angeles:20041022T163000
DTSTART;TZID=America/Los_Angeles:20041022T150000
LOCATION:11 Large
SUMMARY:Like Now: Two Explorations in Deep Lexical Semantics
UID:20041022T150000@NL
URL:http://www.isi.edu/natural-language/nl-seminar
END:VEVENT
BEGIN:VEVENT
DESCRIPTION: Inflected languages in a low-resource setting present a data sparsity problem forstatistical machine translation. In this work, we present a minimallysupervised algorithm for morpheme segmentation on Arabic dialectswhich reduces unknown words at translation time by over 50%, totalvocabulary size by over 40%, and yields a significant increase inBLEU score over a previous state-of-the-art phrase-based statistical MT system.
DTEND;TZID=America/Los_Angeles:20060825T160000
DTSTART;TZID=America/Los_Angeles:20060825T153000
LOCATION:11 Large
SUMMARY:Minimally Supervised Morphological Segmentation with Applications to Machine Translation
UID:20060825T153000@NL
URL:http://www.isi.edu/natural-language/nl-seminar
END:VEVENT
BEGIN:VEVENT
DESCRIPTION: The syntax and semantics of human language can illuminate many individual psychological differences and important dimensions of social interaction. Thus, analysis of language provides important insights into the underlying psychological properties of individuals and groups. Accordingly, psychological and psycholinguistic research has begun incorporating sophisticated representations of semantic content to better understand the connection between word choice and psychological processes. While the majority of language analysis work in psychology has focused on semantics, psychological information is encoded not just in what people say, but how they say it. We introduce ConversAtion level Syntax SImilarity Metric (CASSIM), a novel method for calculating conversation-level syntax similarity. CASSIM estimates the syntax similarity between conversations by automatically generating syntactical representations of the sentences in conversations, estimating the structural differences between them, and calculating an optimized estimate of the conversation-level syntax similarity. Also, we conduct a series of analyses with CASSIM to investigate syntax accommodation in social media discourse. Further, building off of CASSIM, we propose ConversAtion level Syntax SImilarity Metric-Group Representations (CASSIM-GR). This extension builds generalized representations of syntactic structures of documents, thus allowing researchers to distinguish between people and groups based on syntactic differences.Bio: Reihane is a forth year Ph.D student at USC, working with Morteza Dehghani in Computational Social Science Laboratory. She is interested in introducing new methods and computational models to psychology, and more broadly to social sciences. Her work spans the boundary between natural language processing and psychology, as does her intellectual curiosity.
DTEND;TZID=America/Los_Angeles:20170407T160000
DTSTART;TZID=America/Los_Angeles:20170407T150000
LOCATION:11th Floor Large Conference Room [1135]
SUMMARY:ConversAtion level Syntax SImilarity Metric (CASSIM)
UID:20170407T150000@NL
URL:http://www.isi.edu/natural-language/nl-seminar
END:VEVENT
BEGIN:VEVENT
DESCRIPTION: Human incremental sentence processing is the process by which we reada sentence, word-by-word, and ultimately comprehend its meaning. Acentral question in sentence processing research is to understand theprecise nature of the linguistic representations that we constructwhile comprehending a sentence. Experimental evidence demonstratesthat syntactic structure plays a role in these representations. Butopen questions remain about the type of syntactic structure that ismost relevant to the human sentence processing mechanism: is thissyntactic structure sequential or hierarchical? Does it includelexical information (in which case it is "lexicalized"), or is lexicalinformation processed independently from the syntactic structure (inwhich case the syntactic structure is "unlexicalized")?A previous study (Frank and Bod, 2011) compared unlexicalizedsequential and hierarchical models of human sentence processing, andfound that sequential models explain observed human behavior (e.g. eyemovements) during sentence processing better than hierarchical models.The authors concluded that the human sentence processing mechanism isinsensitive to hierarchical syntactic structure.We investigate this claim, and find a picture that is more complicatedthan the one presented by the previous study. First, we show thatlexicalized syntactic models explain observed human behavior duringsentence processing better than unlexicalized syntactic models.Second, we consider a broader set of sequential and hierarchicalmodels, and show that the findings of (Frank and Bod, 2011) do notgeneralize to this broader set. Finally, we show why, even within theset of models considered by (Frank and Bod, 2011), their findings arenot entirely conclusive. Our results indicate that the claim that thehuman sentence processing mechanism is insensitive to hierarchicalsyntactic structure is premature.
DTEND;TZID=America/Los_Angeles:20121010T150000
DTSTART;TZID=America/Los_Angeles:20121010T140000
LOCATION:6th Floor Conference Room [689]
SUMMARY:Sequential vs. hierarchical syntactic models of human sentence processing
UID:20121010T140000@NL
URL:http://www.isi.edu/natural-language/nl-seminar
END:VEVENT
BEGIN:VEVENT
DESCRIPTION: Natural language generation (NLG) is a well studied and still very challenging field in natural language processing. One of the less studied NLG tasks is the generation of creative texts such as jokes, puns, or poems. Multiple reasons contribute to the difficulty of research in this area. First, no immediate application exists for creative language generation. This has made the research on creative NLG extremely diverse, having different goals, assumptions, and constraints. Second, no quantitative measure exists for creative NLG tasks. Consequently, it is often difficult to tune the parameters of creative generation models and drive improvements to these systems. Finally, rule based systems for creative language generation are not yet combined with deep learning methods.In this work, we address these challenges for poetry generation which is one of the main areas of creative language generation. We introduce password poems as a novel application for poetry generation. Furthermore, we combine finite-state machinery with deep learning models in a system for generating poems for any given topic. We introduce a quantitative metric for evaluating the generated poems and build the first interactive poetry generation system that enables users to revise system generated poems by adjusting style configuration settings like alliteration, concreteness and the sentiment of the poem.In order to improve the poetry generation system, we decide to borrow ideas from human literature and develop a poetry translation system. We propose to study human poetry translation and measure the language variation in this process. we will study how human poetry translation is different from human translation in general and whether a translator translates poetry more freely. Then we will use our findings to develop a machine translation system specifically for translating poetry and proposing metrics for evaluating the quality of poetry translation.Bio: Marjan Ghazvininejad is a PhD student at ISI working with Professor Kevin Knight.
DTEND;TZID=America/Los_Angeles:20170818T160000
DTSTART;TZID=America/Los_Angeles:20170818T150000
LOCATION:11th Floor Large Conference Room [1135]
SUMMARY:Neural Creative Language Generation
UID:20170818T150000@NL
URL:http://www.isi.edu/natural-language/nl-seminar
END:VEVENT
BEGIN:VEVENT
DESCRIPTION: 1) In this talk I describe Hafez, a program that generates any number of distinct poems on a user-supplied topic. Poems obey rhythmic and rhyme constraints. I describe the poetry-generation algorithm, give experimental data concerning its parameters, and show its generality with respect to language and poetic form.2) In this work, we present the first results for neuralizing an Unsupervised Hidden Markov Model. We evaluate our approach on tag induction. Our approach outperforms existing generative models and is competitive with the state-of-the-art though with a simpler model easily extended to include additional context.Marjan Ghazvininejad is a PhD student at ISI working with Prof. Kevin Knight.Yonatan Bisk is a Postdoc at ISI working with Prof. Daniel Marcu.
DTEND;TZID=America/Los_Angeles:20161021T160000
DTSTART;TZID=America/Los_Angeles:20161021T150000
LOCATION:6th Floor Large Conference Room [689]
SUMMARY:EMNLP practice talk: 1) Generating Topical Poetry & 2) Unsupervised Neural Hidden Markov Models
UID:20161021T150000@NL
URL:http://www.isi.edu/natural-language/nl-seminar
END:VEVENT
BEGIN:VEVENT
DESCRIPTION: Abstract: Information Extraction (IE) or the algorithmic extraction of named entities, relations and attributes of interest from text-rich data is an important natural language processing task. In this talk, I will discuss the relationship of IE to fine-grained Information Retrieval (IR), especially when the domain of interest is unusual i.e. computationally under-studied, socially consequential and difficult to analyze. In particular, such domains exhibit a significant long-tail effect, and their language models are obfuscated. Using real-world examples and results obtained in recent DARPA MEMEX evaluations, I will discuss how our search system uses semantic strategies to usefully facilitate complex information needs of investigative users in the human trafficking domain, even when IE outputs are extremely noisy. I briefly report recent results obtained from a user study conducted by DARPA, and the lessons learned thereof for both IE and IR research.Bio: Mayank Kejriwal is a computer scientist in the Information integration group at ISI. He received his Ph.D. from the University of Texas at Austin under Daniel P. Miranker. His dissertation involved domain-independent linking and resolving of structured Web entities at scale, and was published as a book in the Studies in the Semantic Web series. At ISI, he is involved in the DARPA MEMEX, LORELEI and D3M projects. His current research sits at the intersection of knowledge graph construction, search, inference and analytics, especially over Web corpora in unusual social domains.
DTEND;TZID=America/Los_Angeles:20170616T160000
DTSTART;TZID=America/Los_Angeles:20170616T150000
LOCATION:11th Floor Large Conference Room [1135]
SUMMARY:From Noisy Information Extraction to Rich Information Retrieval in Unusual Domains
UID:20170616T150000@NL
URL:http://www.isi.edu/natural-language/nl-seminar
END:VEVENT
BEGIN:VEVENT
DESCRIPTION: Many classic problems in natural language processing can be cast as building mapping from a complex input (e.g., a sequence of words) to a complex output (e.g., a syntax tree or semantic graph). This task is challenging both because language is ambiguous (learning difficulties) and represented with discrete combinatorial structures (computational difficulties). Often these are at odds: the features you want to add to decrease learning difficulties cause nontrivial additional structure yielding worse computational difficulties.I will begin by discussing algorithms that side-step the issue of combinatorial blowup and aim to predict an output structure directly. I will then present approaches that explicitly learn to trade-off accuracy and efficiency, applied to a variety of linguistic phenomena. Moreover, I will show that in some cases, we can actually obtain a model that is faster and more accurate by exploiting smarter learning algorithms.Hal's homepage: http://www.umiacs.umd.edu/~hal/
DTEND;TZID=America/Los_Angeles:20140214T160000
DTSTART;TZID=America/Los_Angeles:20140214T150000
LOCATION:11th Floor Large Conference Room [1135]
SUMMARY:Predicting Linguistic Structures Accurately and Efficiently
UID:20140214T150000@NL
URL:http://www.isi.edu/natural-language/nl-seminar
END:VEVENT
BEGIN:VEVENT
DESCRIPTION: Bilingual alignment serves as an integral step and the foundation inthe building of any state-of-the-art statistical machine translationsystem. It enables us to automatically learn and extract translationrules from hundreds of millions of words of bilingual text.Twenty years ago, the research area of machine translation wasbeginning to make use of the increasing availability and speed ofcomputing resources demanded by the ideas of a previous generation,notably Weaver (1949). The IBM translation models -- statisticalmodels for automatic word-to-word translation (Brown et al., 1990;Brown et al., 1993) - spurred a flurry of new statistical andempirical research in this area. They have become ubiquitous in thefield and are easy to train in an unsupervised fashion; Al-Onaizan etal. (1999) and Och and Ney (2003) have given us open-source toolkitsfor this purpose.However, there are many problems that still exist. The work presentedin this thesis proposal will eliminate many of the problems withalignment systems that have persisted for two decades, significantly improving machine translationquality and decidedly advancing the state-of-the-art. In achievingthis goal, we develop new models of bilingual alignment and efficientsearch algorithms for working with such models.
DTEND;TZID=America/Los_Angeles:20101115T170000
DTSTART;TZID=America/Los_Angeles:20101115T160000
LOCATION:4th Floor Conference Room [460]
SUMMARY:Structured Models for Bilingual Alignment (Ph.D. Proposal practice talk)
UID:20101115T160000@NL
URL:http://www.isi.edu/natural-language/nl-seminar
END:VEVENT
BEGIN:VEVENT
DESCRIPTION: Semantic models of data sources and services provide support to automate many tasks such as source discovery, data integration, and service composition, but writing these semantic descriptions by hand is a tedious and time-consuming task. Most of the related work focuses on automatic annotation with classes or properties of source attributes or input and output parameters. However, constructing a source model that includes the relationships between the attributes in addition to their semantic types remains a largely unsolved problem. In this talk, we present a graph-based approach to hypothesize a rich semantic description of a new target source from a set of known sources that have been modeled over the same domain ontology. We exploit the domain ontology and the known source models to build a graph that represents the space of plausible source descriptions. Then, we compute the top k candidates and suggest to the user a ranked list of the semantic models for the new source. The approach takes into account user corrections to learn more accurate semantic descriptions of future data sources. Our evaluation shows that our method produces models that are twice as accurate than the models produced using a state of the art system that does not learn from prior models.Mohsen's webpage: http://www-scf.usc.edu/~taheriya/
DTEND;TZID=America/Los_Angeles:20140117T160000
DTSTART;TZID=America/Los_Angeles:20140117T150000
LOCATION:11th Floor Large Conference Room [1135]
SUMMARY:A Graph-based Approach to Learn Semantic Descriptions of Data Sources
UID:20140117T150000@NL
URL:http://www.isi.edu/natural-language/nl-seminar
END:VEVENT
BEGIN:VEVENT
DESCRIPTION: This talk is about an improved approach for learning dependency parsersfrom treebank data. Our technique is based on two ideas for improvinglarge margin training in the context of dependency parsing. First, weincorporate local constraints that enforce the correctness of eachindividual link, rather than just scoring the global parse tree. Second,to cope with sparse data, we smooth the lexical parameters according totheir underlying word similarities using Laplacian Regularization. Todemonstrate the benefits of our approach, we consider the problem ofparsing Chinese treebank data using only lexical features, that is,without part-of-speech tags or grammatical categories. We achieve stateof the art performance, improving upon current large margin approaches.Here is the link for the paper:http://www.cs.ualberta.ca/~wqin/papers/depar_margin_conll06.pdfAbout the speaker:Qin Iris Wang is a Ph.D. student from the University of Alberta,working with Dekang Lin and Dale Schuurmans. Her research interestsare in natural language processing and machine learning. Specifically,she has been working on dependency parsing using both generative anddiscriminative methods.
DTEND;TZID=America/Los_Angeles:20060728T163000
DTSTART;TZID=America/Los_Angeles:20060728T150000
LOCATION:11 Large
SUMMARY:Improved Large Margin Dependency Parsing via Local Constraints and Laplacian Regularization
UID:20060728T150000@NL
URL:http://www.isi.edu/natural-language/nl-seminar
END:VEVENT
BEGIN:VEVENT
DESCRIPTION: What do we want to learn from a translation competition and how do we learn it with confidence? We argue that a disproportionate focus on ranking competition participants has led to lots of different rankings, but little insight about which rankings we should trust. In response, we provide the first framework that allows an empirical comparison of different analyses of competition results. We then use this framework to compare several analytical models on data from the Workshop on Machine Translation (WMT).
DTEND;TZID=America/Los_Angeles:20130823T160000
DTSTART;TZID=America/Los_Angeles:20130823T150000
LOCATION:11th Floor Large Conference Room [1135]
SUMMARY:Models of Translation Competitions (long paper at ACL2013)
UID:20130823T150000@NL
URL:http://www.isi.edu/natural-language/nl-seminar
END:VEVENT
BEGIN:VEVENT
DESCRIPTION: In this talk, I will describe the research we are undertaking at the Naval Research Laboratory which revolves around chat (such as Internet Relay Chat) and the problems it causes in the military domain. Chat has become a primary means for command and control communications in the US Navy. Unfortunately, its popularity has contributed to the classic problem of information overload. For example, Navy watchstanders monitor multiple chat rooms while simultaneously performing their other monitoring duties (e.g., tactical situation screens and radio communications). Some researchers have proposed how automated techniques can help to alleviate these problems, but very little research has addressed this problem.I will give an overview of the three primary tasks that are the current focus of our research. The first is urgency detection, which involves detecting important chat messages within a dynamic chat stream. The second is summarization, which involves summarizing chat conversations and temporally summarizing sets of chat messages. The third is human-subject studies, which involves simulating a watchstander environment and testing whether our urgency detection and summarization ideas, along with 3D-audio cueing, can aid a watchstander in conducting their duties.Short Bio: David Uthus is a National Research Council Postdoctoral Fellow hosted at the Naval Research Laboratory, where he is currently undertaking research focusing on analyzing multiparticipant chat. He received his PhD (2010) and MSc (2006) from the University of Auckland in New Zealand and his BSc (2004) from the University of California, Davis. His research interests include microtext analysis, machine learning, metaheuristics, heuristic search, and sport scheduling.
DTEND;TZID=America/Los_Angeles:20110805T160000
DTSTART;TZID=America/Los_Angeles:20110805T150000
LOCATION:4th Floor Large Conference Room [460]
SUMMARY:Overcoming Information Overload in Navy Chat
UID:20110805T150000@NL
URL:http://www.isi.edu/natural-language/nl-seminar
END:VEVENT
BEGIN:VEVENT
DESCRIPTION: This talk will introduce languageFractal, an online system for human-augmented machine translation (MT) that aims to incorporate monolingual speakers into the translation pipeline in a cost-effective manner. The essential principle is to take a middle ground between pure MT and a fully crowdsourced approach by augmenting MT results with human corrections in an iterative cycle. To efficiently emit phrases and sentences to users and to effectively explore the space of possible translation options, we propose the use of determinantal point processes (DPPs), which can be used to model subset selection problems in which diversity of the subset is a desirable characteristic.I will provide a brief tutorial on DPPs (including L-ensembles and the structured variant), and I will present an overview of our formulation of DPPs for dynamic programming problems in the context of the human-augmented machine translation pipeline. I will also introduce the languageFractal pilot and pipeline, the full trials of which will run through the 2014-2015 academic year at Harvard University.Bio: Allen Schmaltz is a Ph.D. student in Computer Science in the School of Engineering and Applied Sciences at Harvard University (2013-present; S.M. 2014), working with Stuart Shieber. He is interested in formal, statistical, and human-augmented machine learning approaches for computational linguistics. Before starting his Ph.D. in Computer Science, he completed the better part of an additional Ph.D. in the (quantitative) social sciences at Harvard University (2010-2013), received a M.A. from Stanford University (2010), and received a B.A. from Northwestern University (2006). Earlier in his academic career he also studied at Cornell University and in Yokohama, Japan, among other places.
DTEND;TZID=America/Los_Angeles:20140822T160000
DTSTART;TZID=America/Los_Angeles:20140822T150000
LOCATION:11th Floor Large Conference Room [1135]
SUMMARY:Determinantal Point Processes for Human-Augmented Machine Translation [Intern talk]
UID:20140822T150000@NL
URL:http://www.isi.edu/natural-language/nl-seminar
END:VEVENT
BEGIN:VEVENT
DESCRIPTION: Many research efforts are addressing the problem of enabling automaticsummarization of opinions and assessments stated on the web in productreviews, discussion forums, and blogs. One key difficulty is that relevantassessments scattered throughout web pages are obscured by variations innatural language. In this paper, we focus on a novel aspect of enablingaggregations of assessments of degree to which a given property holds fora given entity (for instance, how touristy is Boston). We presentGrainPile, a user interface for extracting from the web, aggregating andquantifying degree assessments of unconstrained topics. The interfaceprovides a variety of functions: a) identification of dimensions ofcomparison (properties) relevant to a particular entity or set ofentities, b) comparisons of like entities on user-specified properties(for example, which university is more prestigious, Yale or Cornell), c)tracing the derived opinions back to their sources (so that the reasonsfor the opinions can be found). A central contribution in GrainPile is theevaluated demonstration of feasibility of mapping the recognizedexpressions (such as fairly, very, extremely, and so on) to a common scaleof numerical values and aggregating across all the extracted assessmentsto derive an overall assessment of degree. GrainPile’s novelassessment and aggregation of degree expressions is shown to stronglyoutperform an interpretation-free, co-occurrence based method.Full paper:http://www.isi.edu/~timc/papers/IUI06-grainpile-chkl.pdf
DTEND;TZID=America/Los_Angeles:20060126T140000
DTSTART;TZID=America/Los_Angeles:20060126T130000
LOCATION:4th floor
SUMMARY:GrainPile: Deriving Quantitative Overviews of Free Text Assessments on the Web
UID:20060126T130000@NL
URL:http://www.isi.edu/natural-language/nl-seminar
END:VEVENT
BEGIN:VEVENT
DESCRIPTION: Abstract: Exponential growth in electronic health care data has resulted in new opportunities and urgent needs to discover meaningful data-driven representations and patterns of diseases. Recent rise of this research field with more available data and new applications also has introduced several challenges. In this talk, we will present our deep learning solutions to address some of the challenges. First, health care data is inherently heterogeneous, with a variety of missing values and from multiple data sources. We propose variations of Gated Recurrent Unit (GRU) to explore and utilize the informative missingness in health care data, and hierarchical multimodal deep models to utilize the relations between different data sources. Second, model interpretability is not only important but necessary for care providers and clinical experts. We introduce a simple yet effective knowledge distillation approach called interpretable mimic learning to learn interpretable gradient boosting tree models while mimicking the performance of deep learning models.Bio: Zhengping Che is a third year PhD candidate in the Computer Science Department at the University of Southern California, advised by Professor Yan Liu. Before that, he received his bachelor degree in Computer Science from Pilot CS Class (Yao Class) at Tsinghua University, China. His primary research interest lies in the area of deep learning and its applications in health care domain, especially on multivariate time series data.
DTEND;TZID=America/Los_Angeles:20160429T160000
DTSTART;TZID=America/Los_Angeles:20160429T150000
LOCATION:11th Floor Large Conference Room [1135]
SUMMARY:Deep learning solutions to computational phenotyping in health care
UID:20160429T150000@NL
URL:http://www.isi.edu/natural-language/nl-seminar
END:VEVENT
BEGIN:VEVENT
DESCRIPTION: In this paper, we analyze the effect of resampling techniques,including under-sampling and over-sampling used in active learning forword sense disambiguation (WSD). Experimental results show thatunder-sampling causes negative effects on active learning, butover-sampling is a relatively good choice. To alleviate thewithin-class imbalance problem of over-sampling, we propose abootstrap-based over-sampling (BootOS) method that works better thanordinary over-sampling in active learning for WSD. Finally, weinvestigate when to stop active learning, and adopt two strategies,max-confidence and min-error, as stopping conditions for activelearning. According to experimental results, we sug-gest a predictionsolution by considering max-confidence as the upper bound andmin-error as the lower bound for stopping conditions.
DTEND;TZID=America/Los_Angeles:20070601T153000
DTSTART;TZID=America/Los_Angeles:20070601T150000
LOCATION:11 Large
SUMMARY:Active Learning for Word Sense Disambiguation with Methods for Addressing the Class Imbalance Problem
UID:20070601T150000@NL
URL:http://www.isi.edu/natural-language/nl-seminar
END:VEVENT
BEGIN:VEVENT
DESCRIPTION: The Scamseek project aims to build a surveillance tool for identifyingfinancial scams on the Internet by performing document classification ofInternet pages. There are three principle types of documents of concern:those that give financial advice by unregistered advisors, unlawfulinvestment schemes, and share ramping.The first phase of the project has been completed and a working system,known as ScamAlert installed at the Australian Securities and InvestmentCommission (ASIC). The independent audit of the performance of the systemproved satisfactory with a result for precision of .75, recall .43, andF=. 54, along with identification of 4 scams misclassified by the client.Significant improvement in recall is foreshadowed in the 2nd phase of theproject. The results are satisfying in the context of the structure ofthe data where the density of scam documents is about 1.8% of the totalcorpus.The good performance of the operational system is ascribed to thecombination of using a strong linguistic model of language (SystemicFunctional Linguistics) to define the scam documents in parallel with arich statistical analysis of the structure of non-scam documents and scamlook-alikes. A large amount of the experimental program has concentratedon understanding and exploiting the interaction between the linguisticallydescribed aspects of the documents and the statistical properties. Eachtype of data has been used to inform and modify the usage of the other.The operational aspects of the project have proven to be as challenging asthe research objectives. The project has a budget of $2.2M over 15 months.It has been managed so as to create a balance in resources between theneeds of both the research objectives and the engineering objectives.Software development has concentrated on three aspects. Firstly, toproduce an environment for the strong directive management ofcomputational linguistics experiments, secondly, in the aid of thelinguists to create tools to support their manual analysis, and thirdlythe best practice of software engineering principles to ensure a cleanautomated rollout of the production system for ASIC.The contributing partners in the Scamseek project are The Capital MarketsCo-operative Research Centre (CMCRC), ASIC, the University of Sydney andMacquarie University.
DTEND;TZID=America/Los_Angeles:20040325T120000
DTSTART;TZID=America/Los_Angeles:20040325T103000
LOCATION:11 Large
SUMMARY:ScamSeek: Capturing Financial Scams at the Coalface by Language Technology
UID:20040325T103000@NL
URL:http://www.isi.edu/natural-language/nl-seminar
END:VEVENT
BEGIN:VEVENT
DESCRIPTION: I will present work on the interpretation of descriptions of visual scenes such as 'A man is sitting on a chair and using the computer'. One application of this research is the automatic generation of 3D scenes which provides a way for non-artists to create graphical content and have wide-ranging applications in entertainment and education.The core task of text-to-scene generation involves understanding the high-level content of a description and translating it into a low-level representation representing a 3D scene as a set of relations between pre-existing 3D models. Linguistic, spatial, and world-knowledge inference is required in this process on different levels.My talk will present VigNet, a repository of lexical- and world knowledge needed for text-to-scene generation, which is based on FrameNet. I will also describe how visual scenes can be represented as directed graphs and how information in VigNet can be encoded in Synchronous Hyperedge Replacement Grammars to enable semantic parsing and generation of a scene.Bio:Daniel Bauer is a PhD candidate at Columbia University. His research interests include lexical and computational semantics, semantic parsing, and formal grammars in syntax and semantics. He is a co-founder of WordsEye Inc, a company that aims to make text-to-3D-scene generation available to everyone on social media. Daniel is currently an intern at ISI for the second summer in a row. He received his undergrad degree in Cognitive Science from the University of Osnabrück, Germany, and a MSc in Language Science and Technology from Saarland University.
DTEND;TZID=America/Los_Angeles:20130712T160000
DTSTART;TZID=America/Los_Angeles:20130712T150000
LOCATION:11th Floor Large Conference Room [1135]
SUMMARY:Understanding Descriptions of Visual Scenes Using Graph Grammars
UID:20130712T150000@NL
URL:http://www.isi.edu/natural-language/nl-seminar
END:VEVENT
BEGIN:VEVENT
DESCRIPTION: We present a preliminary study on unsupervised preposition sense disambiguation (PSD), comparing different models and training techniques (EM, MAP-EM with L0 norm, Bayesian inference using Gibbs sampling). To our knowledge, this is the ï¬rst attempt at unsupervised preposition sense disambiguation. Ultimately, we want to disambiguate prepositions not by and for themselves, but in the context of sequential semantic labeling. This should also improve disambiguation of the words linked by the prepositions (here, morning, shopped, and Rome). We propose using unsupervised methods in order to leverage unlabeled data, since, to our knowledge, there are no annotated data sets. Our best accuracy for PSD reaches 56%, a signiï¬cant improvement (at p < .001) of 16% over the most-frequent-sense baseline.This is a joint work with Ashish Vaswani, Stephen Tratz, David Chiang, and Eduard Hovy
DTEND;TZID=America/Los_Angeles:20110422T160000
DTSTART;TZID=America/Los_Angeles:20110422T150000
LOCATION:11th Floor Large Conference Room [1135]
SUMMARY:Models and Training for Unsupervised Preposition Sense Disambiguation
UID:20110422T150000@NL
URL:http://www.isi.edu/natural-language/nl-seminar
END:VEVENT
BEGIN:VEVENT
DESCRIPTION: (note: this is a very tentative title -- comments welcome!)We present a novel extension of syntax-directed translation forstatistical MT. Formally speaking, our model is based on tree-to- stringtransducers that recursively convert a parse-tree in the source-languageinto a string in the target-language. These transduction rules havemulti-level trees on the source-side, giving this system moretransformational power due to the extended domain of locality. We alsopresent efficient algorithms for decoding based on dynamic programming.Initial experiments on English-to-Chinese translation show promisingresults in both speed and the translation quality.Joint work with Kevin Knight and Aravind Joshi.Bio:Liang Huang is a 3rd-year PhD student from the University of Pennsylvania.He is mainly interested in algorithms and formalisms for parsing andsyntax-based machine translation. His recent work has been on k-bestparsing algorithms (with David Chiang) and synchronous binarization for MT(with Hao Zhang, Dan Gildea, and Kevin Knight).
DTEND;TZID=America/Los_Angeles:20060303T163000
DTSTART;TZID=America/Los_Angeles:20060303T150000
LOCATION:11th Floor (Large)
SUMMARY:Syntax-Directed Translation with Extended Domain of Locality
UID:20060303T150000@NL
URL:http://www.isi.edu/natural-language/nl-seminar
END:VEVENT
BEGIN:VEVENT
DESCRIPTION: This talk will cover two topics. The first part will be a brief overview of Manuel's recent project in abbreviation disambiguation. Following, Manuel will give a brief overview of how various NLP methods are used in an industrial setting in a danish company that provides text analytics services for publishers such as Springer-Nature.Bio: Manuel is a 3rd year PhD student at Aarhus University in Denmark. His PhD is focused on applying Data Mining and Machine Learning on large collections of unstructured text documents with the goal of extracting and representing knowledge embedded in the documents.
DTEND;TZID=America/Los_Angeles:20180208T120000
DTSTART;TZID=America/Los_Angeles:20180208T110000
LOCATION:Conference Room [689]
SUMMARY:Abbreviation Disambiguation and NLP Deployment in Industrial Settings
UID:20180208T110000@NL
URL:http://www.isi.edu/natural-language/nl-seminar
END:VEVENT
BEGIN:VEVENT
DESCRIPTION: We present a method to transliterate names in the framework ofend-to-end statistical machine translation. The system is trained tolearn when to transliterate.For Arabic to English MT, we developed and trained a transliterator on abitext of 7 million sentences and Google's English terabyte ngrams andachieved better name translation accuracy than 3 out of 4 professionaltranslators. The talk also includes a discussion of challenges in nametranslation evaluation.
DTEND;TZID=America/Los_Angeles:20080404T160000
DTSTART;TZID=America/Los_Angeles:20080404T150000
LOCATION:11 Large
SUMMARY:Name Translation in Statistical Machine Translation: Learning When to Transliterate
UID:20080404T150000@NL
URL:http://www.isi.edu/natural-language/nl-seminar
END:VEVENT
BEGIN:VEVENT
DESCRIPTION: Online activity is characterized by diurnal and weekly patterns, reflecting human circadian rhythms, sleep cycles, and social patterns of work and leisure. Using data from online social networking site Facebook, we uncover temporal patterns that take place at far shorter time scales. Specifically, we demonstrate fine-grained, within-session behavioral changes, where a session is defined as a period of time a user engages with Facebook before choosing to take a break. We show that over the course of a session, users spend less time consuming some types of content, such as textual posts, and preferentially consume more photos and videos. Moreover, users who spend more time engaging with Facebook have different patterns of session activity than the less-engaged users, a distinction that is already visible at the start of the session. We study activity patterns with respect to usersâ demographic characteristics, such as age and gender, and show that age has a strong impact on within-session behavioral changes. Finally, we show that the temporal patterns we uncover help us more accurately predict the length of sessions on Facebook.Bio. I am a third-year Computer Science PhD student at the University of Southern California (USC), Information Sciences Institute (ISI) working under the supervision of Kristina Lerman. My main research interest is the study of large and complex datasets, especially data from online social networks, which includes the measurement and analysis of users' behavior in OSNs. I'm currently a Data Science intern at Facebook in Menlo Park.Before joining USC, I got my master's from Max Planck Institute for Software Systems (MPI-SWS), Germany. I worked with Krishna Gummadi as my advisor and also with Meeyoung Cha (KAIST) and Winter Mason (Facebook) during my master's. Before MPI, I got my bachelor's in Computer Engineering (Software) from University of Tehran, Iran.
DTEND;TZID=America/Los_Angeles:20151023T160000
DTSTART;TZID=America/Los_Angeles:20151023T150000
LOCATION:6th Floor Large Conference Room [689]
SUMMARY:Fine-grained Temporal Patterns of Online Content Consumption
UID:20151023T150000@NL
URL:http://www.isi.edu/natural-language/nl-seminar
END:VEVENT
BEGIN:VEVENT
DESCRIPTION: We present a novel method to detect parallel fragments within noisy parallel corpora. Isolating these parallel fragments from the noisy data in which they are contained frees us from noisy alignments and stray links that can severely constrain translation-rule extraction. We do this with existing machinery, making use of an existing word alignment model for this task. We evaluate the quality and utility of the extracted data on large-scale Chinese-English and Arabic-English translation tasks and show significant improvements over a state-of-the-art baseline.
DTEND;TZID=America/Los_Angeles:20120518T153000
DTSTART;TZID=America/Los_Angeles:20120518T150000
LOCATION:11th Floor Large Conference Room [1135]
SUMMARY:Automatic Parallel Fragment Extraction From Noisy Data (NAACL HLT Practice Talk)
UID:20120518T150000@NL
URL:http://www.isi.edu/natural-language/nl-seminar
END:VEVENT
BEGIN:VEVENT
DESCRIPTION: Human behavior is exceedingly complex. Its expression and experience are inherently multimodal, and are characterized by individual and contextual heterogeneity. The confluence of sensing, communication and computing is however allowing access to data, in diverse forms and modalities, that is enabling us understand and model human behavior in ways that were unimaginable even a few years ago. No domain exemplifies these opportunities more than that related to human health and wellbeing. Consider for example the domain of Autism where crucial diagnostic information comes from manually-analyzed audiovisual data of verbal and nonverbal behavior. Behavioral signal processing advances can enable not only new possibilities for gathering data in a variety of settings--from laboratory and clinics to free living conditions--but in offering computational models to advance evidence-driven theory and practice.This talk will describe our ongoing efforts on Behavioral Signal Processing (BSP)--technology and algorithms for quantitatively and objectively understanding typical, atypical and distressed human behavior--with a specific focus on communicative, affective and social behavior. Using examples drawn from different application domains, the talk will also illustrate Behavioral Informatics applications of these processing techniques that contribute to quantifying higher-level, often subjectively described, human behavior in a domain-sensitive fashion.[Work supported by NIH, NSF, DARPA, and ONR].Biography of the Speaker:Shrikanth (Shri) Narayanan is Andrew J. Viterbi Professor of Engineering at USC, where he is Professor of Electrical Engineering, and, jointly in, Computer Science, Linguistics and Psychology. Prior to USC he was with AT&T Bell Labs and AT&T Research. His research focuses on human-centered information processing and communication technologies. He is a Fellow of the Acoustical Society of America, IEEE, and the American Association for the Advancement of Science (AAAS). Shri Narayanan is an Editor for the Computer, Speech and Language Journal and an Associate Editor for the IEEE Transactions on Multimedia, the IEEE Transactions on Affective Computing and the Journal of Acoustical Society of America having previously served an Associate Editor for the IEEE Transactions of Speech and Audio Processing (2000-2004) and the IEEE Signal Processing Magazine (2005-2008). He is a recipient of several honors including the 2005 and 2009 Best Paper awards from the IEEE Signal Processing Society and serving as its Distinguished Lecturer for 2010-11. With his students, he has received a number of best paper awards including winning the Interspeech Challenges in 2009 (Emotion classification), 2011 (Speaker state classification) and in 2012 (Speaker trait classification). He has published over 500 papers and has 13 U.S. patents.
DTEND;TZID=America/Los_Angeles:20130124T160000
DTSTART;TZID=America/Los_Angeles:20130124T150000
LOCATION:6th Floor Conference Room [689]
SUMMARY:Behavioral Signal Processing: Deriving Human Behavioral Informatics from Multimodal Signals
UID:20130124T150000@NL
URL:http://www.isi.edu/natural-language/nl-seminar
END:VEVENT
BEGIN:VEVENT
DESCRIPTION: In this talk, I look at how the notion of discourse coherence can bemodeled computationally. I begin with the following idea: if you takea text and shuffle its sentences into a random order, that text willno longer make sense. In other words, the text will be "incoherent".Our task is to learn how to reassemble a shuffled text into an orderthat humans would consider to be coherent.I discuss practical and theoretical motivations for the task,evaluations of our model, increases in performance achieved over thesummer, and directions for future research.This work was done in collaboration with Kevin Knight, Daniel Marcu,Jonathan Graehl and Nick Mote.
DTEND;TZID=America/Los_Angeles:20030912T160000
DTSTART;TZID=America/Los_Angeles:20030912T143000
LOCATION:11 Large
SUMMARY:Discourse Coherence for Ordering Information
UID:20030912T143000@NL
URL:http://www.isi.edu/natural-language/nl-seminar
END:VEVENT
BEGIN:VEVENT
DESCRIPTION: Statistical machine translation (SMT) has witnessed promising progress in recent years. Typically, an SMT system is characterized as a single-best pipeline, whose modules are independent to each other and only take as input single-best results from the previous module. With this assumption, each module will inevitably introduce errors in single-best outputs, which will propagate and accumulate along the pipeline, and eventually hurt the translation quality.In order to alleviate this problem, we use compact structures such as lattices and forests instead of single-best results in each module, and then integrate both lattice and forest into a single tree-to-string system. We explore the algorithms of lattice parsing, lattice-forest-based rule extraction and decoding. Experiments show a statistically significant improvement over a start-of-the-art forest-based baseline. More interestingly, we observe a significant reduction in rule-set size when extracting with a lattice, which implies better generalizability (with a smaller model).About the speaker:Haitao Mi is an Assistant Researcher in the Institute of Computing Technology, Chinese Academy of Sciences (CAS/ICT). He received his Ph.D. from CAS/ICT in 2009. His main research interests include syntax-based machine translation and statistical parsing. Additional information about him and his group can be found at http://nlp.ict.ac.cn/~mihaitao/
DTEND;TZID=America/Los_Angeles:20100331T160000
DTSTART;TZID=America/Los_Angeles:20100331T150000
LOCATION:11th Floor Large Conference Room [1135]
SUMMARY:Lattice and Forest for SMT
UID:20100331T150000@NL
URL:http://www.isi.edu/natural-language/nl-seminar
END:VEVENT
BEGIN:VEVENT
DESCRIPTION: We introduce dependency relations into deciphering foreign languages and show that dependency relations help improve the state-of-the-art deciphering accuracy by over 500%. We learn a translation lexicon from large amounts of genuinely non parallel data with decipherment to improve a phrase-based machine translation system trained with limited parallel data. In experiments, we observe BLEU gains of 1.2 to 1.8 across three different test sets.
DTEND;TZID=America/Los_Angeles:20131016T120000
DTSTART;TZID=America/Los_Angeles:20131016T110000
LOCATION:6th Floor Large Conference Room [Rm # 689]
SUMMARY:Dependency Based Decipherment for Resource-Limited Machine Translation (EMNLP2013 practice talk)