text_gen_4_comp_ling.ipynb

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    "colab": {
      "name": "text-gen-4-comp-ling.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "machine_shape": "hm",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Nikoschenk/language_model_finetuning/blob/master/text_gen_4_comp_ling.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qK_lPvRlBdMB",
        "colab_type": "text"
      },
      "source": [
        "# Finetuning Transformers on Texts (Domain: Computational Linguistics) for Text Generation\n",
        "This notebook demonstrates how to fine-tune GPT-2 on the custom texts (ACL reference corpus) using the [Hugging Face Transformer](https://github.com/huggingface/transformers) library, and includes examples for text generation comparing both the base model and the fine-tuned model."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "biV1z0koaDHT",
        "colab_type": "text"
      },
      "source": [
        "## Installation"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EGscdaCtpmbV",
        "colab_type": "text"
      },
      "source": [
        "### Install the HuggingFace Transfomers library."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uicio9FLPv5j",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Install transformers.\n",
        "!git clone https://github.com/huggingface/transformers\n",
        "\n",
        "import os\n",
        "os.chdir('/content/transformers')\n",
        "\n",
        "# Use language modeling version as of April 21st.\n",
        "!git checkout b1ff0b2ae7d368b7db3a8a8472a29cc195d278d8\n",
        "\n",
        "!pip install .\n",
        "!pip install -r ./examples/requirements.txt\n",
        "\n",
        "os.chdir('/content/transformers/examples')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "weh0BoPfk1zc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import torch\n",
        "import run_language_modeling\n",
        "import run_generation\n",
        "import collections\n",
        "import random\n",
        "import numpy as np\n",
        "\n",
        "from transformers import AutoConfig\n",
        "from transformers import AutoTokenizer\n",
        "from transformers import AutoModelWithLMHead\n",
        "\n",
        "# Make sure that this version of transformers has the correct evaluate functionality.\n",
        "from run_language_modeling import evaluate"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aX2LEl-uwMXp",
        "colab_type": "text"
      },
      "source": [
        "### Mount your Google Drive\n",
        "Checkpoints will be saved Google Drive folders."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oQ6FFHiMMP0V",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-itAiZE-iqHG",
        "colab_type": "text"
      },
      "source": [
        "Download the CL introductions for fine-tuning."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wA5VE9tiil6Q",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!mkdir '/content/drive/My Drive/Comp-Ling'\n",
        "!wget -nc -O '/content/drive/My Drive/Comp-Ling/training-texts.txt' https://raw.githubusercontent.com/Nikoschenk/language_model_finetuning/master/acl_papers/introductions-train.txt\n",
        "!wget -nc -O '/content/drive/My Drive/Comp-Ling/validation-texts.txt' https://raw.githubusercontent.com/Nikoschenk/language_model_finetuning/master/acl_papers/introductions-valid.txt"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PjbGRezolTDX",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 210
        },
        "outputId": "40be6235-ad42-4c9e-ebad-a6bb2a77ebf4"
      },
      "source": [
        "# Make sure they were downloaded properly.\n",
        "# Display ten introductions.\n",
        "!tail -n 10 '/content/drive/My Drive/Comp-Ling/training-texts.txt'"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Networked computers can be used to support learning in various ways. In computational linguistics, the predominant pattern of use is twofold: Learning materials are distributed using hypertext, and laboratories are conducted in which students work directly with computational linguistics processors such as parsers and generators. The 'authorware' approach to developing learning materials has not been popular in the teaching of computational linguistics because of the extensive labour involved in encoding content. Since CL is all about the use of powerful general mechanisms and expressive formalisms, the idea of writing learning materials using less expressive tools has little appeal. However, the new technologies of the internet make it easier to combine media to produce integrated learning environments in which pedagogical materials can be intimately connected to mechanisms and resources. Using such approaches can produce payoffs whether or not distance learning is involved. A better integrated set of resources for laboratory activities makes fewer demands on support staff such as graduate demonstrators. The ability to encapsulate mechanisms and tools in applets also means that the need to maintain special purpose laboratories is diminished, and it is also possible to promote CL to potential students in schools. This paper reports experience with the use of web browsers to provide practical activities to an introductory class of computational linguistics students. We concentrate on the tools developed locally, although we make use of others where appropriate. Much of the discussion focuses on what is possible with the constraints imposed by current network software.\n",
            "There are two traditional distinctions in higher education that are currently on a period of big debate. The first one concerns industrial training vs higher education objectives, the second one is related to the role of emerging technologies for distance learning, potentially blurring the until now clear separation between conventional universities and distance learning Institutions. Changes in both Education and Training contexts are moving closer their previously separate objectives. On one hand, there is much concern about bringing educational curricula more in line with vocational needs. In a variety of disciplines, higher education courses are becoming oriented to professionally recognized qualifications, adopting approaches to integrate practice in context. On the other hand, industrial organizations are looking for a more versatile way of building personal profiles to adapt individuals to the changing needs of their organizations. Thus, acquiring more general skill is been increasingly addressed for advanced training purposes. Distance learning Universities are operating since the seventies. Most of them were based on the industrial model characterized by the production of highly effective learning materials for independent study and the use of \"one-way\" media, such as print, video, radio or TV broadcasting. While in traditional education the cost of education depends on the number of students involved, this is not the case with the industrial distance education model. The cost depends on a fixed part for the preparation of materials with less investment in academic staff for tutoring tasks. A clear improvement of using new technology for this model has been the delivery of course materials through CD-ROM, and later on through the Internet. Computerbased materials can integrate presentations with simulations, problem solving tools, virtual laboratories and the like, to engage students in a process of active learning. More controversial is the embedding of human-human interactive technologies. It is an open challenge to find new ways of satisfying increasing interaction between students and tutors when staff resources remain scarce. Peer collaboration is an idea to explore, but as it is the case in many industrial organizations collaborative behavior does not happen spontaneously just because the technology is available, but rather is a shift of culture to be established. The virtual campus [1] is a metaphor currently deserving a lot of attention. It is sometimes presented as a bridge for conventional universities td extend their scope to reach distance learners, sometimes as an opportunity for distance learning institutions to provide an environment combining the strength of systematic teaehing with elaborated materials to support more ' efficiently distance study. But metaphors should be carefully contrasted with the demand side perspective to foresee whether technology-ba§ed distance teaching can succeed. For example, 'there is, despite the quite short history of desktop computer conferencing, some failed pilot experiences in real-time multipoint teleteaching. The cost of equipment was a wellknown factor, but another very practical issue was often neglected: real distance students are in fact fully reluctant to participate regularly in synchronous events, with a fixed schedule. The existing technology has demonstrated to be powerful. Paying due regard to usability issues in order to realize its potential for learning purposes remains an open question. Over the last decade the European Commission has launched a variety of programs to encourage international partnership and cooperation in the field of education and training. Some of the programs aimed at analyzing the current situation to recommend future action. Others were projects strongly technology oriented to develop platforms and environments specially focused on distance and flexible learning. In addition, a set of pilot experiences and applications were also implemented. Within the SOCRATES framework [2], ACO*HUM [3] is a thematic network including a working group on Computational linguistics and language engineering. A plan for developing open distance learning pilot courses was proposed in cooperation with ELSNET [4], and finally a proposal including six pilots was launched in February 98. We have developed one of them, on the topic of Information Retrieval (IR) and Natural Language Processing (NLP) [5]. This topic is especially well suited for a learning-by-doing web course. The Internet itself is the biggest IR testbed, and the web search engines are the most powerful applications of IR techniques. The students can be guided through on-line NLP software to manipulate, expand, translate queries, etc., and get first hand impressions on the utility of such processes. Next section discusses our approach while a detailed presentation is given in section three.\n",
            "We describe here an approach to inducing selectional preferences from text corpora. In the traditional view, a predicate constrains its arguments by selecting for particular semantic classes, or concepts. Selectional restriction of the traditional sort can be characterized as a relation p(v, r, c) over predicates v, syntactic roles r, and argument concepts c. Individual instances (v, r, c) are selectional tuples. Examples are given in table 1. Of more interest to computational linguistics is selectional preference, a continuous-valued generalization of selectional restriction. Selectional preference is a mapping a : (v, r, c) a that maps each tuple (v, r, c) to a real number a, the degree Predicate Role Argument Class splatter subj CAUSAL-AGENT splatter obj FLUID splatter on SURFACE Table 1: Selectional tuples of preference of v for c with respect to role r. Positive degrees of preference are intended to correlate with intuitive judgments of \"plausibility\" or \"typicality,\" and negative judgments are intended to correlate with intuitive judgments of \"implausibility.\" We have chosen to characterize such selectional preference as a side-effect of a stochastic model for generating what we will call co-occurrence tuples: triples (v, r, n) for v a predicate, r a syntactic role, and n the headword of the argument filling the role r with respect to v. An example of a co-occurrence tuple is (splatter, obj, water). Co-occurrence tuples can be obtained from text corpora, and can be used to make inferences about the probability of selectional tuples. For example, the co-occurrence tuple (splatter, obj, water) may be taken as evidence for the selectional tuple (splatter, obj, FLUID). More concretely, such co-occurrence tuples make up the training corpora, from which we train our stochastic models. For this study, we have used the British National Corpus (100M words), from which we have extracted co-occurrence tuples using the Cass parser (Abney, 1997). By way of illustration, table 2 shows the values of n in tuples (eat, obj, n) along with their frequencies in the corpus. This \"subcorpus\" would be used to train a stochastic model specific to the object role of the verb eat and is the first of two inputs to our induction process. There are two problems with such training data: it is noisy and it contains ambiguity. The noise is sometimes due to tagging or parsing errors, and sometimes due to metaphorical uses. Examples from 1 meat 45 bucket 1 ice 2 tape 1 investment 1 soup 2 proportion 2 kitchen 1 fry 4 root 4 salad 2 top 1 bread 14 feast 1 scrap 2 majority 2 sauce 1 sugar 1 principle 1 food 77 hole 2 roll 4 pack 1 bag 2 race 1 mouthful 3 dinner 11 sheep 1 salt 1 meal 46 trout 2 pasta 1 slice 7 dish 2 spaghetti 6 chicken 5 stick 1 egg 18 average 1 sandwich 13 yogurt 1 mustard 1 breakfast 30 garlic 1 Table 2: Objects of eat in the BNC table 2 include investment, average, tape, and race. However, note that the \"good\" examples such as food and meal are much greater in number and frequency. Thus, the signal is stronger than the noise in most cases and most reasonably robust training methods will be able to handle the noise. The second problem, that of word sense ambiguity, is more difficult. The word bread in table 2 provides an example. Bread can be used to refer to a food, e.g., the multigrain bread in Germany is wonderful, but it can also refer to money, e.g., I could really use some bread since my car just broke down. For this reason, it is not immediately clear which concepts the 14 tokens of bread provide evidence for. If the wrong choice is made for a high frequency word, incorrect selectional preferences will result. The model we propose represents this sort of uncertainty in a natural way: the two senses of bread are represented as different paths through a stochastic model, both of which generate the same observation. This stochastic model is a hidden Markov model (HMM) which has the shape of a given semantic hierarchy. Figure 1 shows an example hierarchy. In the work discussed here, we made use of the WordNet semantic hierarchy (Miller, 1990). This hierarchy is the second input to our induction process. We hoped that the forward-backward algorithm, an EM algorithm, would properly disambiguate word senses in the training data as a side effect of its quest to maximize the likelihood of the training data given the model. However, for reasons we will discuss in section 4, this was not the case. In the following section we discuss work on selectional preference induction that also assumes as input (i) subcorpora corresponding to predicate role pair and (ii) a semantic class hierarchy. Then we TOP LOCATION ENTITY LIFE-FORM CAUSAL-AGENT .../' PERSON BEE — mortal WORKER 4 ..„ BEE Figure 1: Example Semantic Class Hierarchy formally define our stochastic model. Next we look at a number of ultimately unsuccessful attempts to modify the forward-backward algorithm to perform effective word-sense disambiguation of the training data. Despite these problems we did obtain some encouraging results which we present at the end of the paper.\n",
            "The objective of this paper is to analyse the applicability of statistical and learning methods to automated grapheme-phoneme alignment in Japanese, without reliance on pre-annotated training data or any form of supervision. The two principal models proposed herein are a simple statistical model nonreliant on learning techniques, and an incremental learning method deriving therefrom, incorporating automated \"pseudo-supervision\" drawing on prior alignments. The incremental learning method selects a single alignment candidate to accept at each iteration, and adjusts the statistical model accordingly to aid in the subsequent disambiguation of residue G-P tuples. Grapheme-phoneme (\"G-P\") alignment is defined as the task of maximally segmenting a grapheme compound into morpho-phonic units, and aligning each unit to the corresponding substring in the phoneme compound (Bilac et al., 1999). Its main use is in portrayal of the phonological interaction between adjoining grapheme segments, and also implicit description of the range of readings each grapheme segment can take. We further suggest that a large-scale database of maximally aligned G-P tuples has applications within the more conventional task of G-P translation (Klatt, 1987; Huang et al., 1994; Divay and Vitale, 1997). Our particular interest in developing a database of G-P tuples is to apply it in the development of a kanji tester which can dynamically predict plausibly incorrect readings for a given grapheme string. For this purpose, we require as great a coverage of grapheme strings as possible, and the proposed system has thus been designed to exhaustively align the input set of G-P tuples, sacrificing precision for 100% recall. 'Grapheme string' in this research refers to the maximal kanji representation of a given word or compound, and 'phoneme string' refers to the kana (hiragana and/or katakana) mora correlate.' By 'maximal' segmentation is meant that the grapheme string must be segmented to the degree that each segment corresponds to a self-contained component of the phonemic description of that compound, and that no segment can be further segmented into aligning sub-segments. The statement of `maximality' of segmentation is qualified by the condition that each segment must constitute a morpho-phonic unit, in that for conjugating parts-of-speech, namely verbs and adjectives, the conjugating suffix must be contained in the same segment as the stem. By way of illustration of the alignment process, let us consider the example of the verb ka-n-syasu-ru [S-At-su-ru] \"to thank/be thankful\" ,2 a portion of the 35 member alignment paradigm for which is given in Fig. 1. The importance of maximality of alignment is observable by way of a/ign35, which constitutes a legal (under-)alignment of the correct solution in aligni. Here, there is scope for further segmentation, as evidenced by the replaceability of IA by its phoneme content of ka-n in isolation of Nt (producing the string ka-n-X-sn-ru). Thus, we are able to discount align35 on the grounds of it being non-maximal. That a segment exists between sya and su-ru, on the other hand, is a result of su-ru being a light verb and hence an independent morpheme. The overall alignment procedure is depicted in 1 Our description of kana as phoneme units represents a slight abuse of terminology, in that individual kana characters are uniquely associated with a broad phonetic transcription potentially extending over multiple phones. Note, however, that in abstracting away to this meta-phonemic representation, we are freed from consideration of low-level phonological concerns such as phoneme connection constraints. 25o as to make this paper as accessible as possible to readers not familiar with Japanese, hiragana and katakana characters have been transliterated into Latin script throughout this paper and are essentially treated as being identical. The graphemic kanji character set, on the other, has been provided in its original form to give the reader a feel for the significance of the kana-kanji dichotomy. For both the grapheme and phoneme strings, character boundaries are indicated by \"-\" and segment boundaries (which double as character boundaries) indicated by \"0\". 9 (1,) 4 (:)su-ru ha-n 0 sya 1 ha 0 n-sya-su-riz IS-24-su-ru ha-n-sya-su-ru aligni aligns aligns align35 Figure 1: Candidate alignments for 03-4/-.0-ru [ka-n-sya-su-ru] \"to thank/be thankful\" Fig. 2. Within input set W, the system proceeds by first generating an exhaustive listing of all alignment candidates (PSseg)—(GSseg) for each G-P tuple i. This alignment paradigm is pruned through application of a series of constraints, and either of the two proposed alignment selection methods is then applied to identify a single most plausible alignment from each alignment paradigm. Both the simple statistical model (\"method-1\") and incremental learning method (\"method-2\") rely on a slightly modified form of the TF-IDF model. In the case of method-1, statistical analysis is applied to the full range of alignment paradigms in W and all alignment paradigms are disambiguated in parallel. For method-2, we commence identically to method-1, but single out an alignment paradigm to disambiguate at each iteration, and incrementally adjust the statistical model based on both the reduced W and the expanded c.o. As such, the principal difference between the two methods can be stated as statistical feedback from co to 'zi) in method-2, but not in method-1. Figure 2: An outline of the system In the remainder of this paper, we first present the methodology used to derive all legal alignments for a given G-P tuple (Section 2), then give full details of both the simple statistical method and incremental learning method (Section 3), before evaluating the various methods against a baseline rule-based method (Section 4). Finally, in Section 5, we consider additional applications of the basic methodology proposed here.\n",
            "Much recent research in the field of natural language processing has focused on an empirical, corpusbased approach, and the high accuracy achieved by a corpus-based approach to part-of-speech tagging and parsing has inspired similar approaches to word sense disambiguation. For the most successful approaches to such problems, correctly annotated materials are crucial for training learning-based algorithms. Regardless of whether or not learning is involved, the prevailing evaluation methodology requires correct test sets in order to rigorously assess the quality of algorithms and compare their performance. This seems to require manual tagging of the training corpus with appropriate sense for each occurrence of an ambiguous word. However, in marked contrast to annotated training material for part-of-speech tagging, (a) there is no coarse-level set of sense distinctions widely agreed upon (whereas * This work was supported in part by KISTEP for Soft Science Research project. headword : open' sense usage examples open Open the window a bit, please. He opened the door for me to come in. Open the box. start Our chairman opened the conference by welcoming new delegates/ Open a public meeting. Table 1: The entry of open(vt.) in OALD part-of-speech tag sets tend to differ in the detail); (b) sense annotation has a comparatively high error rate (Miller, personal communication, reports an upper bound for human annotators of around 90% for ambiguous cases, using a non-blind evaluation method that may make even this estimate overly optimistic(Resnik, 1997)); (c) in conclusion, a sense-tagged corpus large enough to achieve broad coverage and high accuracy word sense disambiguation is not available at present. This paper describes an unsupervised sense disambiguation system using a POS-tagged corpus and a machine-readable dictionary (MRD). The system we propose circumvents the need for the sense-tagged corpus by using MRD's usage examples as the sense-tagged examples. Because these usage examples show the natural examples for headword's each sense, we can acquire useful sense disambiguation context from them. For example, open has several senses and usage examples for its each sense listed in a dictionary as shown in Table 1. The words within usage examples window, door, box, conference, and meeting are useful context for sense disambiguation of open. Another problem that is common for much corpusbased work is data sparseness, and the problem especially severe for work in WSD. First, enormous amounts of text are required to ensure that all senses of a polysemous word are represented, given the vast disparity in frequency among senses. In addition, the many possible co-occurrences for a given polysemous word are unlikely to be found in even a very large corpus, or they occur too infrequently to be significant. In this paper, we propose two methods 17 that attack the problem of data sparseness in W! using small corpus and dictionary. First, extendi word similarity measures from direct co-occurren, to co-occurrences of co-occurred words, we comp the word similarities using not co-occurred wol but co-occurred clusters. Second, we acquire IS relations of nouns from the MRD definitions. D tionary definitions of nouns are normally written such a way that one can identify for each headwc (the word being defined), a \"genus term\" (a wc more general that the headword), and these are lated via an IS-A relation(Amsler, 1979). It is poE ble to cluster the nouns roughly by the identificati of the IS-A relationship.\n",
            "Development of electronic morphological resources has undergone several decades of research. The first morphological analyzers focussed on inflectional processes (inflection, for English, mainly covers verb conjugation, and number and gender variations). With the development of Information Retrieval, people have looked for ways to build simple analyzers which are able to recognize the stem of a given word (thus addressing both inflection and derivation'). These analyzers are known as stemmers. Faced with the increasing demand for natural language processing tools for a variety of languages, people have searched for procedures to (semi)automatically acquire morphological resources. On the one hand, we find work from the IR community aimed at building robust stemmers without much attention given to the morphological processes of a language. Most of this work relies on a list of affixes, usually built by the system developer, and a set of rules to stem words (Lovins, 1968; Porter, 1980). Some of these works fit within an unsupervised setting, (Hafer and Weiss, 1974; Adamson and Boreham, 1974) and to a certain extent (Jacquemin and Tzoukerman, 1997), but do not directly address the problem of learning morphological processes. On the other hand, some researchers from the computational linguistics community have developed techniques to learn affixes of a language and software to segment words according to the identified elements. The work described in (Daelemans et al., 1999) is a good example of this trend, based on a supervised 1 The distinction between inflectional and derivational morphology is far from clearcut. However, in practice, such a distinction allows one to divide the problems at hand and was implicitly adopted in our lexicon development plan. learning approach. However, it is difficult in most of these studies to infer the underlying linguistic framework assumed. We present in this paper an unsupervised method to learn suffixation operations of a language from an inflectional lexicon. This method also leads to the development of a stemming procedure for the language under consideration. Section 2 presents the linguistic view we adopt on derivation. Section 3 describes the preliminary steps of our learning method and constitutes the core of our stemming procedure. Finally, section 4 describes the learning of suffixation operations. 2 Derivation in a language The derivational processes of a language allow speakers of that language to analyse and generate new words. Most recent linguistic theories view these processes as operations defined on words to produce words. From a linguistic point of view, a word can be represented as an element made of several independent layers (feature structures, for example, could be chosen for this representation. We do not want to focus on a particular formalism here, but rather to explain the model we will adopt). The different layers and the information they contain vary from one author to the other. We adopt here the layers used in (Fradin, 1994), as exemplified on the French noun table: (G) table (F) (teibl) (M) fem-sg (SX) (S) table where (G) corresponds to the graphemic form of the word, (F) to the phonological form, and (M), (SX) and (S) respectively contain morphological, syntactic and semantic information. A derivation process then operates on such a structure to produce a new structure. Each layer of the original structure is transformed via this operation. We can adopt the following probabilistic model to account for such a derivation process: 24 p(w,,wo .Ep(w2(G) = OPG(tvi(G)), Op W2(F) = OpF(Wl(F)), w2(M) = w2(S = Opsx(wi(SX)),w2(S) = OPs(tvi(S))) where Op is a derivation process, OPG is the component of Op which operates on the graphemic layer, and w(G) is the graphemic layer associated to word w. The different layers can be divided up into three main dimensions, used in linguistic studies to identify and classify suffixes of a language: the formal dimension (corresponding to G and F), the morphosyntactic dimension (M and SX), and the semantic dimension (5). The nature of the operation along these dimensions mainly depend on the language under consideration. For example, for Indo-European languages, for the formal dimension, a suffixation operation consists in the concatenation of a suffix to the original form. Morphographemic as well as phonological rules are then applied to turn the ideal form obtained via concatenation into a valid surface form. We focus in this article on concatenative languages, id est languages for which derivation corresponds, for the formal dimension, to a concatenation operation. We also restrict ourselves to the study of suffixes and suffixation. Nevertheless, the principles and methods we use can be extended to non-concatenative languages and prefixes. The aim of the current work is two-fold. On the one hand we want to develop stemming procedures for Information Retrieval. On the other hand, we want to develop methods to assist lexicographers in the development of derivational lexicons. We posses inflectional lexicons for a variety of different languages, and we'll use these lexicons as input to our system. Furthermore, we are interested in making the method as language independent as possible, which means that we will explore languages without a. priori knowledge2 and thus we wish to rely on an unsupervised learning framework. The probabilistic model above suggests that, in order to learn suffixation operations of a language, one should look at word pairs ('WI, w,) of that language for which w2 derives from w1. In an unsupervised setting, such pairs are not directly accessible, and we .need to find ways to extract the information we are interested in from a set of pairs the words of which are not always related via derivation. The method we designed first builds, for a given language, relational families, which are an approximation of derivational families. These families 210 particular, it is interesting to avoid relying on suffix lists, which vary from one author to the other. are then used to produce pairs of words which are a first approximation of the pairs of related words. From this set of pairs, we then extract suffixes and suffixation operations. The next section addresses the construction of relational families.\n",
            "Choosing the correct translation of a content word in context, referred to as \"translation disambiguation (of content word)\", is a key task in machine translation. It is also crucial in cross-language text processing including cross-language information retrieval and abstraction. Due to the recent availability of large text corpora, various statistical approaches have been tried including using 1) parallel corpora (Brown et al., 1990), (Brown et al., 1991), (Brown, 1997), 2) non-parallel bilingual corpora tagged with topic area (Yamabana et al., 1998) and 3) un-tagged mono-language corpora in the target language (Dagan and Itai, 1994), (Tanaka and Iwasaki, 1996), (Kikui, 1998). A problem with the first two approaches is that it is not easy to obtain sufficiently large parallel or manually tagged corpora for the pair of languages targeted. Although the third approach eases the problem of preparing corpora, it suffers from a lack of useful information in the source language. For example, suppose the proper name, \"Dodgers\", provides good context to identify the usage of \"hit\" in the training corpus in English. If the translation of \"Dodgers\" rarely occurs in the target language corpora, it does not contribute to target word selection. The method presented in this paper solves this problem by choosing the target word that corresponds to the usage identified in the source language corpora. This method is totally unsupervised in the sense that it acquires disambiguation information from non-parallel bilingual corpora (preferably in the same domain) free from tagging. It combines two unsupervised disambiguation algorithms: one is the word sense disambiguation algorithm based on distributional clustering(Schuetze, 1997) and the other is the translation disambiguation algorithm using target language corpora(Kikui, 1998). For the given word in context, the former algorithm identifies its usage as one of several predefined usage classes derived by clustering a large amount of usages in the source language corpus. The latter algorithm is responsible for associating each usage class (i.e., cluster) with a target word that best expresses the usage. The following sections are organized as follows. In Section 2, we overview the entire method. The following two sections (i.e., Section 3 and 4) then introduce the two major components of the method including the two unsupervised disambiguation algorithms. Section 5 and 6 are devoted respectively to a preliminary evaluation and discussions on related research.\n",
            "With surprising frequency, archaeologists dig up documents that no modern person can read. Sometimes the written characters are familiar (say, the Phoenician alphabet), but the language is unknown. Other times, it is the reverse: the written script is unfamiliar but the language is known. Or, both script and language may be unknown. Cryptanalysts also encounter unreadable documents, but they try to read them anyway. With patience, insight, and computer power, they often succeed. Archaeologists and linguists known as epigraphers apply analogous techniques to ancient documents. Their decipherment work can have many resources as input, not all of which will be present in a given case: (1) monolingual inscriptions, (2) accompanying pictures or diagrams, (3) bilingual inscriptions, (4) the historical record, (5) physical artifacts, (6) bilingual dictionaries, (7) informal grammars, etc. In this paper, we investigate computational approaches to deciphering unknown scripts, and report experimental results. We concentrate on the following case: • unfamiliar script • known language • minimal input (monolingual inscriptions only) This situation has arisen in many famous cases of decipherment—for example, in the Linear B documents from Crete (which turned out to be a \"non-Greek\" script for writing ancient Greek) and in the Mayan documents from Mesoamerica. Both of these cases lay unsolved until the latter half of the 20th century (Chadwick, 1958; Coe, 1993). In computational linguistic terms, this decipherment task is not really translation, but rather text-to-speech conversion. The goal of the decipherment is to \"make the text speak,\" after which it can be interpreted, translated, etc. Of course, even after an ancient document is phonetically rendered, it will still contain many unknown words and strange constructions. Making the text speak is therefore only the beginning of the story, but it is a crucial step. Unfortunately, current text-to-speech systems cannot be applied directly, because they require up front a clearly specified sound/writing connection. For example, a system designer may create a large pronunciation dictionary (for English or Chinese) or a set of manually constructed character-based pronunciation rules (for Spanish or Italian). But in decipherment, this connection is unknown! It is exactly what we must discover through analysis. There are no rule books, and literate informants are long-since dead.\n",
            "When provided with enough labeled training examples, a variety of text classification algorithms can learn reasonably accurate classifiers (Lewis, 1998; Joachims, 1998; Yang, 1999; Cohen and Singer, 1996). However, when applied to complex domains with many classes, these algorithms often require extremely large training sets to provide useful classification accuracy. Creating these sets of labeled data is tedious and expensive, since typically they must be labeled by a person. This leads us to consider learning algorithms that do not require such large amounts of labeled data.\n",
            "In this paper we discuss a potential solution to two problems in Natural Language Processing (NLP), using a combination of statistical and symbolic machine learning techniques. The first problem is learning the syntactic roles, or categories, of words of a language i.e. learning a lexicon. Secondly, we discuss a method of annotating a corpus with parses. The aim is to learn Categorial Grammar (CG) lexicons, starting from a set of lexical categories, the functional application rules of CG and an unannotated corpus of positive examples. The CG formalism (discussed in Section 2) is chosen because it assigns distinct categories to words of different types, and the categories describe the exact syntactic role each word can play in a sentence. This problem is similar to the unsupervised part of speech tagging work of, for example, Brill (Brill, 1997) and Kupiec (Kupiec, 1992). In Brill's work a lexicon containing the parts of speech available to each word is provided and a simple tagger attaches a complex tag to each word in the corpus, which represents all the possible tags that word can have. Transformation rules are then learned which use the context of a word to determine which simple tag it should be assigned. The results are good, generally achieving around 95% accuracy on large corpora such as the Penn Treebank. Kupiec (Kupiec, 1992) uses an unsupervised version of the Baum-Welch algorithm, which is a way of using examples to iteratively estimate the probabilities of a Hidden Markov Model for part of speech tagging. Instead of supplying a lexicon, he places the words in equivalence classes. Words in the same equivalence class must take one of a specific set of parts of speech. This improves the accuracy of this algorithm to about the same level as Brill's approach. In both cases, the learner is provided with a large amount of background knowledge - either a complete lexicon or set of equivalence classes. In the approach presented here, the most that is provided is a small partial lexicon. In fact the system learns the lexicon. The second problem - annotating the corpus - is solved because of the approach we use to learn the lexicon. The system uses parsing to determine which are the correct lexical entries for a word, thus annotating the corpus with the parse derivations (also providing less probable parses if desired). An example of another approach to doing this is the Fidditch parser of Hindle (Hindle, 1983) (based on the deterministic parser of Marcus (Marcus, 1980)), which was used to annotate the Penn Treebank (Marcus et al., 1993). However, instead of learning the lexicon, a complete grammar and lexicon 59 must be supplied to the Fidditch parser. Our work also relates to CG induction, which has been attempted by a number of people. Osborne (Osborne, 1997) has an algorithm that learns a grammar for sequences of part-of-speech tags from a tagged corpora, using the Minimum Description Length (MDL) principle - a welldefined form of compression. While this is a supervised setting of the problem, the use of the more formal approach to compression is of interest for future work. Also, results of 97% coverage are impressive, even though the problem is rather simpler. Kanazawa (Kanazawa, 1994) and Buszkowski (Buszkowski, 1987) use a unification based approach with a corpus annotated with semantic structure, which in CG is a strong indicator of the syntactic structure. Unfortunately, they do not present results of experiments on natural language corpora and again the approach is essentially supervised. Two unsupervised approaches to learning CGs are presented by Adriaans (Adriaans, 1992) and Solomon (Solomon, 1991). Adriaans, describes a purely symbolic method that uses the context of words to define their category. An oracle is required for the learner to test its hypotheses, thus providing negative evidence. This would seem to be awkward from a engineering view point i.e. how one could provide an oracle to achieve this, and implausible from a psychological point of view, as humans do not seem to receive such evidence (Pinker, 1990). Unfortunately, again no results on natural language corpora seem to be available. Solomon's approach (Solomon, 1991) uses unannotated corpora, to build lexicons for simple CG. He uses a simple corpora of sentences from children's books, with a slightly ad hoc and non-incremental, heuristic approach to developing categories for words. The results show that a wide range of categories can be learned, but the current algorithm, as the author admits, is probably too naive to scale up to working on full corpora. No results on the coverage of the CGs learned are provided. In Section 3 we discuss our learner. In Section 4 we describe experiments on three corpora containing examples of a subset of English and Section 5 contains the results, which are encouraging with respect to both problems. Finally, in Section 6, we compare the results with the systems mentioned above and discuss ways the system can be expanded and larger scale experiments may be carried out. Next, however, we describe Categorial Grammar.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8VzV8iTrphJl",
        "colab_type": "text"
      },
      "source": [
        "## Finetune and evaluate"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xOzFhwDSqOg3",
        "colab_type": "text"
      },
      "source": [
        "### Launch fine-tuninng\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C33GutF1QVEV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Your models will be stored here.\n",
        "!mkdir '/content/drive/My Drive/Comp-Ling-Models'\n",
        "\n",
        "# GPT-2 fine-tuning.\n",
        "!python run_language_modeling.py \\\n",
        "    --output_dir='/content/drive/My Drive/Comp-Ling-Models' \\\n",
        "    --model_type=gpt2 \\\n",
        "    --model_name_or_path=gpt2-medium \\\n",
        "    --save_total_limit=1 \\\n",
        "    --num_train_epochs=10.0 \\\n",
        "    --do_train \\\n",
        "    --evaluate_during_training \\\n",
        "    --logging_steps=2000 \\\n",
        "    --save_steps=2000 \\\n",
        "    --train_data_file='/content/drive/My Drive/Comp-Ling/training-texts.txt' \\\n",
        "    --do_eval \\\n",
        "    --eval_data_file='/content/drive/My Drive/Comp-Ling/validation-texts.txt' \\\n",
        "    --per_gpu_train_batch_size=2 \\\n",
        "    --per_gpu_eval_batch_size=2 \\\n",
        "    --block_size=128 \\\n",
        "    --gradient_accumulation_steps=5"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q9mAiosB2wBm",
        "colab_type": "text"
      },
      "source": [
        "### Compute perplexity of a dataset.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eiqSgGhtcDNd",
        "colab_type": "text"
      },
      "source": [
        "#### Look at what checkpoints are available\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zk_qHytBIETo",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!ls '/content/drive/My Drive/Comp-Ling-Models/'"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TRFwV1Ry3Evk",
        "colab_type": "text"
      },
      "source": [
        "#### Helper functions"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sc2VCFBG3pFf",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def load_model(args):\n",
        "  \"\"\"Creates a model and loads in weights for it.\"\"\"\n",
        "  config = AutoConfig.from_pretrained(args.model_name_or_path, cache_dir=None)\n",
        "\n",
        "  model = AutoModelWithLMHead.from_pretrained(\n",
        "      args.model_name_or_path,\n",
        "      from_tf=bool(\".ckpt\" in args.model_name_or_path),\n",
        "      config=config,\n",
        "      cache_dir=None\n",
        "  )\n",
        "  \n",
        "  model.to(args.device)\n",
        "  return model\n",
        "\n",
        "def set_seed(seed):\n",
        "  \"\"\"Set the random seed.\"\"\"\n",
        "  random.seed(seed)\n",
        "  np.random.seed(seed)\n",
        "  torch.manual_seed(seed)\n",
        "  if args.n_gpu > 0:\n",
        "    torch.cuda.manual_seed_all(args.seed)\n",
        "\n",
        "def do_perplexity_eval(args, model, data_file_path):\n",
        "  \"\"\"Computes the perplexity of the text in data_file_path according to the provided model.\"\"\"\n",
        "  set_seed(args.seed)\n",
        "\n",
        "  args.eval_data_file=data_file_path\n",
        "\n",
        "  tokenizer = AutoTokenizer.from_pretrained(args.model_name_or_path, cache_dir=None)\n",
        "\n",
        "  args.block_size = min(args.block_size, tokenizer.max_len)\n",
        "\n",
        "  result = run_language_modeling.evaluate(args, model, tokenizer, prefix=\"\")\n",
        "  return result"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9_F0AHkq4jlv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def dict_to_obj(dic):\n",
        "    \"\"\"Convert a dict to an object\"\"\"\n",
        "    # https://stackoverflow.com/questions/1305532/convert-nested-python-dict-to-object\n",
        "    top = type('dummy', (object,), dic)\n",
        "    seqs = tuple, list, set, frozenset\n",
        "    for i, j in dic.items():\n",
        "        if isinstance(j, dict):\n",
        "            setattr(top, i, dict_to_obj(j))\n",
        "        elif isinstance(j, seqs):\n",
        "            setattr(top, i, type(j)(dict_to_obj(sj) if isinstance(sj, dict) else sj for sj in j))\n",
        "        else:\n",
        "            setattr(top, i, j)\n",
        "    return top"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ERCKSncEBYgJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# GPT-2 EVALUATION:\n",
        "\n",
        "# 1. Fine-tuned model.\n",
        "CHECKPOINT_PATH = '/content/drive/My Drive/Comp-Ling-Models/checkpoint-38000'\n",
        "\n",
        "# 2. Non-fine-tuned model.\n",
        "#CHECKPOINT_PATH = \"gpt2-medium\" \n",
        "\n",
        "# Set this to the list of text files you want to evaluate the perplexity of.\n",
        "DATA_PATHS = ['/content/drive/My Drive/Comp-Ling/validation-texts.txt']\n",
        "\n",
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "n_gpu = torch.cuda.device_count()\n",
        "print(\"Running on device: \", device)\n",
        "\n",
        "args = collections.defaultdict(\n",
        "  model_name_or_path=CHECKPOINT_PATH,\n",
        "  output_dir=CHECKPOINT_PATH,\n",
        "  block_size = 128,\n",
        "  local_rank=-1,\n",
        "  eval_batch_size=2,\n",
        "  per_gpu_eval_batch_size=2,\n",
        "  n_gpu=n_gpu,\n",
        "  mlm=False,\n",
        "  device=device,\n",
        "  line_by_line=False,\n",
        "  overwrite_cache=None,\n",
        "  model_type='gpt2',\n",
        "  seed=42,\n",
        ")\n",
        "args = dict_to_obj(args)\n",
        "\n",
        "model = load_model(args)\n",
        "\n",
        "for data_path in DATA_PATHS:\n",
        "  eval_results = do_perplexity_eval(args, model, data_path)\n",
        "  perplexity = eval_results['perplexity']\n",
        "  print('{} is the perplexity of {} according to {}'.format(\n",
        "      perplexity, data_path, CHECKPOINT_PATH))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r5o7v2hmhMTO",
        "colab_type": "text"
      },
      "source": [
        "### Generate samples\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XcvySe_wrCWh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def generate_samples(args, model, prompt_text):\n",
        "  \"\"\"Generating sampling for the provided prompt using the provided model.\"\"\"\n",
        "  set_seed(args.seed)\n",
        "\n",
        "  tokenizer = AutoTokenizer.from_pretrained(args.model_name_or_path, cache_dir=None)\n",
        "\n",
        "  requires_preprocessing = args.model_type in run_generation.PREPROCESSING_FUNCTIONS.keys()\n",
        "  encoded_prompt = tokenizer.encode(prompt_text, add_special_tokens=False, return_tensors=\"pt\")\n",
        "  encoded_prompt = encoded_prompt.to(args.device)\n",
        "\n",
        "  output_sequences = model.generate(\n",
        "      input_ids=encoded_prompt,\n",
        "      max_length=args.length + len(encoded_prompt[0]),\n",
        "      temperature=args.temperature,\n",
        "      top_k=args.k,\n",
        "      top_p=args.p,\n",
        "      repetition_penalty=args.repetition_penalty,\n",
        "      do_sample=True,\n",
        "      num_return_sequences=args.num_return_sequences,\n",
        "  )\n",
        "\n",
        "  # Remove the batch dimension when returning multiple sequences\n",
        "  if len(output_sequences.shape) > 2:\n",
        "    output_sequences.squeeze_()\n",
        "\n",
        "  generated_sequences = []\n",
        "\n",
        "  for generated_sequence_idx, generated_sequence in enumerate(output_sequences):\n",
        "    generated_sequence = generated_sequence.tolist()\n",
        "\n",
        "    # Decode text\n",
        "    text = tokenizer.decode(generated_sequence, clean_up_tokenization_spaces=True)\n",
        "\n",
        "    # Remove all text after the stop token\n",
        "    text = text[: text.find(args.stop_token) if args.stop_token else None]\n",
        "\n",
        "    # Remove the excess text that was used for pre-processing\n",
        "    text = text[len(tokenizer.decode(encoded_prompt[0], clean_up_tokenization_spaces=True)) :]\n",
        "\n",
        "    # Add the prompt at the beginning of the sequence.\n",
        "    total_sequence = prompt_text + text\n",
        "\n",
        "    generated_sequences.append(total_sequence)\n",
        "\n",
        "  return generated_sequences"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s3LKo9VVjHw0",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "3b0332cc-784c-4a92-961a-d9157e999437"
      },
      "source": [
        "# GPT-2 text generation:\n",
        "\n",
        "# Set this to the checkpoint you want to use for generation, or to \"gpt2-medium\"\n",
        "# to generate with the pre-trained model without finetuning.\n",
        "CHECKPOINT_PATH = '/content/drive/My Drive/Comp-Ling-Models/checkpoint-38000'\n",
        "#CHECKPOINT_PATH = 'gpt2-medium'\n",
        "\n",
        "# Specify your prompt here.\n",
        "PROMPT = '''This text is computer-generated. It is unique because'''\n",
        "\n",
        "\n",
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "n_gpu = torch.cuda.device_count()\n",
        "print(\"Running on device: \", device)\n",
        "\n",
        "args = collections.defaultdict(\n",
        "  model_name_or_path=CHECKPOINT_PATH,\n",
        "  output_dir=CHECKPOINT_PATH,\n",
        "  n_gpu=n_gpu,\n",
        "  mlm=False,\n",
        "  device=device,\n",
        "  model_type='gpt2',\n",
        "  seed=42,\n",
        "  stop_token=None, # Set this if your dataset has a special word that indicates the end of a text.\n",
        "  temperature=1.0,  # temperature sampling. Set this to temperature=1.0 to not use temperature.\n",
        "  k=50,  # k for top-k sampling. Set this to k=0 to not use top-k.\n",
        "  p=1.0,  # p for nucleus sampling. Set this to p=1.0 to not use nucleus sampling.\n",
        "  repetition_penalty=None,\n",
        "  length=750,  # Number of tokens to generate.\n",
        "  num_return_sequences=20,  # Number of independently computed samples to generate.\n",
        ")\n",
        "args = dict_to_obj(args)\n",
        "\n",
        "model = load_model(args)\n",
        "sequences = generate_samples(args, model, PROMPT)\n",
        "for idx, sequence in enumerate(sequences):\n",
        "  print('\\n====== GENERATION {} ======'.format(idx))\n",
        "  print(sequence)"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "08/03/2020 09:33:34 - INFO - transformers.configuration_utils -   loading configuration file /content/drive/My Drive/Comp-Ling-Models/checkpoint-38000/config.json\n",
            "08/03/2020 09:33:34 - INFO - transformers.configuration_utils -   Model config GPT2Config {\n",
            "  \"activation_function\": \"gelu_new\",\n",
            "  \"architectures\": [\n",
            "    \"GPT2LMHeadModel\"\n",
            "  ],\n",
            "  \"attn_pdrop\": 0.1,\n",
            "  \"bos_token_id\": 50256,\n",
            "  \"embd_pdrop\": 0.1,\n",
            "  \"eos_token_id\": 50256,\n",
            "  \"initializer_range\": 0.02,\n",
            "  \"layer_norm_epsilon\": 1e-05,\n",
            "  \"model_type\": \"gpt2\",\n",
            "  \"n_ctx\": 1024,\n",
            "  \"n_embd\": 1024,\n",
            "  \"n_head\": 16,\n",
            "  \"n_layer\": 24,\n",
            "  \"n_positions\": 1024,\n",
            "  \"n_special\": 0,\n",
            "  \"predict_special_tokens\": true,\n",
            "  \"resid_pdrop\": 0.1,\n",
            "  \"summary_activation\": null,\n",
            "  \"summary_first_dropout\": 0.1,\n",
            "  \"summary_proj_to_labels\": true,\n",
            "  \"summary_type\": \"cls_index\",\n",
            "  \"summary_use_proj\": true,\n",
            "  \"task_specific_params\": {\n",
            "    \"text-generation\": {\n",
            "      \"do_sample\": true,\n",
            "      \"max_length\": 50\n",
            "    }\n",
            "  },\n",
            "  \"vocab_size\": 50257\n",
            "}\n",
            "\n",
            "08/03/2020 09:33:34 - INFO - transformers.modeling_utils -   loading weights file /content/drive/My Drive/Comp-Ling-Models/checkpoint-38000/pytorch_model.bin\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Running on device:  cuda\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "08/03/2020 09:33:48 - INFO - transformers.configuration_utils -   loading configuration file /content/drive/My Drive/Comp-Ling-Models/checkpoint-38000/config.json\n",
            "08/03/2020 09:33:48 - INFO - transformers.configuration_utils -   Model config GPT2Config {\n",
            "  \"activation_function\": \"gelu_new\",\n",
            "  \"architectures\": [\n",
            "    \"GPT2LMHeadModel\"\n",
            "  ],\n",
            "  \"attn_pdrop\": 0.1,\n",
            "  \"bos_token_id\": 50256,\n",
            "  \"embd_pdrop\": 0.1,\n",
            "  \"eos_token_id\": 50256,\n",
            "  \"initializer_range\": 0.02,\n",
            "  \"layer_norm_epsilon\": 1e-05,\n",
            "  \"model_type\": \"gpt2\",\n",
            "  \"n_ctx\": 1024,\n",
            "  \"n_embd\": 1024,\n",
            "  \"n_head\": 16,\n",
            "  \"n_layer\": 24,\n",
            "  \"n_positions\": 1024,\n",
            "  \"n_special\": 0,\n",
            "  \"predict_special_tokens\": true,\n",
            "  \"resid_pdrop\": 0.1,\n",
            "  \"summary_activation\": null,\n",
            "  \"summary_first_dropout\": 0.1,\n",
            "  \"summary_proj_to_labels\": true,\n",
            "  \"summary_type\": \"cls_index\",\n",
            "  \"summary_use_proj\": true,\n",
            "  \"task_specific_params\": {\n",
            "    \"text-generation\": {\n",
            "      \"do_sample\": true,\n",
            "      \"max_length\": 50\n",
            "    }\n",
            "  },\n",
            "  \"vocab_size\": 50257\n",
            "}\n",
            "\n",
            "08/03/2020 09:33:48 - INFO - transformers.tokenization_utils -   Model name '/content/drive/My Drive/Comp-Ling-Models/checkpoint-38000' not found in model shortcut name list (gpt2, gpt2-medium, gpt2-large, gpt2-xl, distilgpt2). Assuming '/content/drive/My Drive/Comp-Ling-Models/checkpoint-38000' is a path, a model identifier, or url to a directory containing tokenizer files.\n",
            "08/03/2020 09:33:48 - INFO - transformers.tokenization_utils -   Didn't find file /content/drive/My Drive/Comp-Ling-Models/checkpoint-38000/added_tokens.json. We won't load it.\n",
            "08/03/2020 09:33:48 - INFO - transformers.tokenization_utils -   loading file /content/drive/My Drive/Comp-Ling-Models/checkpoint-38000/vocab.json\n",
            "08/03/2020 09:33:48 - INFO - transformers.tokenization_utils -   loading file /content/drive/My Drive/Comp-Ling-Models/checkpoint-38000/merges.txt\n",
            "08/03/2020 09:33:48 - INFO - transformers.tokenization_utils -   loading file None\n",
            "08/03/2020 09:33:48 - INFO - transformers.tokenization_utils -   loading file /content/drive/My Drive/Comp-Ling-Models/checkpoint-38000/special_tokens_map.json\n",
            "08/03/2020 09:33:48 - INFO - transformers.tokenization_utils -   loading file /content/drive/My Drive/Comp-Ling-Models/checkpoint-38000/tokenizer_config.json\n",
            "08/03/2020 09:33:48 - WARNING - transformers.modeling_utils -   Setting `pad_token_id` to 50256 (first `eos_token_id`) to generate sequence\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "====== GENERATION 0 ======\n",
            "This text is computer-generated. It is unique because it was written in natural English, Spanish, and Swedish, under the time and place restrictions imposed by the military machine translation system MADAAT. The system had previously been used to translate English from English into Spanish and from English into Swedish. MADAAT was used in five different conditions: a) to translate into Spanish, b) for translating Spanish into French, c) to translate into Swedish, d) to translate into Danish/English, and finally e) to translate into French, Swedish and Danish. For French, Swedish and Danish, the MADAAT system was augmented using language models from the statistical machine translation system, MOSSUM. In this paper, we examine three basic parameters of translation methods in terms of their role in translating unseen text. A new machine translation system, MADAAT, is introduced using the MADAAT method with three parameters: translation cost, input speed, input accuracy, and translation quality. MADAAT significantly outperforms other systems on test data for all parameters evaluated.\n",
            "The idea of using text mining to reduce the effort of human user of text has been around for about ten years and was pioneered by TREC (Radev et al., 2000). However, it has received less attention compared to problems and tools that are common in NLP research communities that are not specialized in linguistics or computer science, such as Machine Translation, Information Retrieval, or Information Extraction, but which require deeper understanding or higher levels of domain knowledge. While it is more difficult for an IE system to build the required context for extracting data from one domain to another, machine translation techniques can be used to create the necessary knowledge automatically. Translating text between languages without using linguistic knowledge such as machine translation has also recently received increased attention. Because of the success of these techniques in NLP, there exists a growing need for better ways to learn machine translation of texts from corpora. But how do we learn automatic machine translation without human intervention? In computational linguistics, knowledge is one tool which one acquires in order to be able to understand another language and produce a translation of a text in that language without knowledge in the first language itself. This paper addresses this difficult problem, and specifically introduces a new method for learning automatic text translation from multiple corpus and language pairs. The method incorporates the use of linguistic knowledge into an NLP context and performs the translation as a combination of context-free rulebased method (CFR) and stochastic learning. The key idea is to take advantage of the structure of both the source and target texts, the structure of the corpus to be translated, and the source and target languages in the knowledge sources (source-side versus target-side) of such systems with the goal of minimizing the effort of the human user. In this respect, the new technique is a departure from previous attempts in the last years. The main contributions of this paper are: 1) we address the question of why text translation but not automatic translation, 2) we are able to create a new type of resource with language labels and translations that are more useful for transliteration by the use of statistical machine translation method. We demonstrate that the CFT method can be used for text mining tasks and thus offer a better generalization to NLP tasks. The following three experiments are presented and discussed: 1) A large-scale comparison of techniques for learning machine translation from multiple corpora and multiple languages from multiple sources from web and from multiple domains (2) a web corpora created without human intervention and (A) and the use of source-side features for the translation of the texts (B).\n",
            "Information retrieval is a very appealing in NLP. The field of information retrieval is at this\n",
            "\n",
            "====== GENERATION 1 ======\n",
            "This text is computer-generated. It is unique because the authors have taken the trouble to correct, for the benefit of the readership, some of the apparent inconsistencies. The resulting text is written for an audience that has already learned the basic concepts and uses the same notation for analyzing the mathematical model, hence it is self-contained. Therefore all the text in this textbook applies directly in the course's course. The introductory chapter is one of the most interesting from a practical perspective. It presents the mathematical model with a rather technical presentation: A. Theorem 2 In NLC we can compute an ordered set of functions to describe sets of concepts (i.e., an ordered set of functions). This may be viewed as a logical extension of the notion of equality (e.g. in Eq. 1), and thus represents an extension to the mathematical model. This is the first formal treatment of the notion of a logical formula into a finite set of functions, which gives the formal rigidity and interpretability of natural language and is therefore the basis of an adequate explanation of natural language. In particular, the meaning of a formula can be directly inferred from its context without recourse to reference to formulas. A. b. A set B is a set of functions from sets Ix to Iy where A such that Ix is an all-valued symbol for each I. This requires an all-valued functor of Ix to be a finite set. This is important if the system is intended audience is to understand sets rather than sets of natural language sentences. This is important because it is the basis on which some of the natural language constructs are described. In the second paragraph it states that all of the proof-net theory of natural language is based on A-calculus, just like an algebra is based on A. c. There is an infinite set of functions such that it is impossible to compose a formula into another formula, as long as there is no A between these. The introduction emphasizes this fact. This is also important because the formalization provides formal language for representing natural language, rather than it provides a mathematical structure for expressing natural language semantics. A mathematical model. In the Appendix A the logical form is employed in describing the mathematical model. We need a notation for expressing natural language that is both complete and concise. There is no need to enumerate all the features one could describe with a formula, but it is clear why we do have to. In particular, the notation cannot describe every possible logical form of a formula and must be a natural extension of the formal language in which the model is stated in. The model is called the algebra. In addition, the model could also be interpreted by a mathematical theory as a formula expressing the set of all possible mappings between sets or to some other symbol. As the introduction indicates, the algebra is much different from the formal model used in a model of a speaker's language. The model of the speaker's language provides the necessary machinery for presenting natural language, where the interpretation of natural language can be specified by the application of a formula to an input formula expressed as described in the algebra rather than through use of a formula. Another important aspect is that the algebra provides a framework for expressing the model. This framework for understanding natural language can be thought of as a mathematical framework which allows the model to be extended to a model of natural languages. This means that the algebra must be able to refer to the speaker and the speaker to the system by means of a formula. There are several reasons why this is an important aspect of the mathematics. First, for the natural language component of an intelligent search system. We often use the model of the speaker within the system, as a model of the speaker and a model of the speaker. Furthermore, a\n",
            "\n",
            "====== GENERATION 2 ======\n",
            "This text is computer-generated. It is unique because it is unique because it is the first written in the English spoken language. In its current state, however, no computer program exists that translates written text into another natural language. In order to develop an NL interface computer programs have to be designed from a variety of sources. They have to be trained, they have to be coded, and they have to be tested. One of the sources of uncertainty about these programs is the performance of the program at the \"translation\" stage. A good algorithm must be able to deal with the enormous variety of tasks that have to be performed. For that reason some of the problems encountered when attempting to train a particular machine translation system have proved to be quite different from those encountered in translating text. Some methods, such as the one used in this paper, are simple and elegant. Others, such as \"tunnichs\" (McDonald and Pereira [1987], \"preliminary experiments\" using the approach described in this paper, have shown that it is possible to do much more than just translating text from one language into another (Brown, Pereira and Matsumoto [1992], Pereira [1993), Langkilde-Geary [1993], \"trees in depth\" approach which makes use bilingual information in their translation system to produce output trees for a target language as well as sentence structures) are somewhat more complex. The next-generation. It is not entirely clear how different techniques can be combined to produce a full-blown NL interface, but both seem to make specific problems less difficult. Thus most of the approaches presented here produce very small fragments, with no apparent end goal, leaving the issue of translation unclear. The research presented here is preliminary. The final goal of this project is to give a well-defined system as a prototype for several possible ways to make use of this \"basic input/output system.\" Our present understanding of the problems involved is that they can be divided roughly into two categories, which can be solved efficiently, and that there is little value in trying to do both at the same time. 1 We will assume that a grammar is developed and an interface is built by first generating a set of input trees, then extracting phrase structures. The process is then extended with a grammar. We will attempt to solve some of the problems involved in this phase of development, for the same reasons why it came to be developed. We will not address syntactic analysis and translation issues. However, we will discuss techniques for automatically generated text generation which are used in some of the proposals introduced here. The remainder of this paper is organized as follows. Section 2 compares the three generation methods. In Section 3, a description of an NL system for machine translation is presented which uses syntactically motivated rules to produce output structures. Section 4 provides an overview of \"trees as input/output\" approach, and outlines the possible \"trees\" that can be built from the input/output system. In section 5, we discuss two problems which arise in translating text from one language into another. In section 6, algorithms for automatic generation of both phenomena are presented. We also discuss our approach to both problems.\n",
            "There may be several possibilities for computer systems capable of providing assistance to users in a particular domain in their own language, and how they handle communication between them. There is certainly no one algorithm which is more amenable as a vehicle for assistance to any domain than any other, since the best answers can be provided from different sources, more widely dispersed, points of view. What is provided in the context of a computer systems which try to determine how to communicate with users while taking as their context whatever information as they have (or can be provided for them in their environments, i..\n",
            "\n",
            "====== GENERATION 3 ======\n",
            "This text is computer-generated. It is unique because it does not depend on any linguistic conventions, as opposed to other studies which rely on them. More importantly, it is unique because it does not make a decision about the meaning of an unknown word by making reference to the knowledge sources, such as WordNet or thesauri. This allows the reader to infer the meaning of an unknown word that appears in the context without consulting information structure (e.g., lexical items and morphosyntactic features). It also allows a text generator for this language to infer how a word should be modified or morphologically analyzed. This last characteristic of the generator, and one of the distinguishing features of the generator above, has to do with the way the input is transformed into output. Given all of the above characteristics and the generality of the language, it is important for the output to be sufficiently diverse. For example, in the NLU system that used in this paper, the input is often very abstract and linguistically impoverished, with noun phrases that must be transformed into noun phrases and inflected to fill in semantic information slots. Given all of the above characteristics, it is important that the output is sufficiently diverse for the system to be appropriately ambiguous. This also means that there is enough information in the input to make the resulting input sufficiently ambiguous with respect to linguistic constraints. There are several ways one could interpret a noun phrase can stand in for a noun phrase. Some include prepositional phrases, prepositional phrases (i.e., the noun phrases used for identifying certain entities), noun phrase noun phrases (also known as conjunctions), or conjunctions or conjunction to specify the semantics of some noun phrase. In the remainder of this paper, the language being developed uses simple relative clauses, relative clauses, clauses, and clausal compounds. In the context of general NLU, the reader must determine the relative clause and all conjugations that serve as modifiers of any noun phrase in the input. However, not all of these relations will be expressed explicitly in the sentence; it can be quite complicated. When syntactic features are appropriate, this information must be expressed by means of auxiliary verb phrases. The approach chosen for this paper, based on the technique introduced by (DeRose 89), is intended to facilitate the representation of the semantics of the sentence as a predicate and to make it easy to interpret the sentence without the linguistic knowledge that is present in the input (i.e., identifying the lexical, semantic, semantic, and syntactic properties of the input). This paper proposes a method in which an NP is an example of the latter class of verb phrases, relative clauses, that express the semantics of English sentences. It is also intended to be a method of encoding the predicate and an auxiliary verb phrase. The remainder of this paper will be organized as follows. Section 2 gives an overview of related work. In section 3, an overall description of the input is given of noun phrases and relative clauses in the input. This section describes the parser, the method for recognizing an NP that expresses the semantics of the input, and the system used in a parsing system based on this work. Section. Section 4 presents the results of an experiment used in an evaluation of the system and a summary of the results. In section 5 we survey related approaches to the generation of English sentences. In section 6 the structure of an English sentence is presented. Finally, section 7 presents some issues and some conclusions.\n",
            "A large number of factors influence the ability of human readers to understand the semantics of natural language and its semantic content. Among these are the factors, syntactic structure, the number of words, and syntactic features and their interactions. The problem of the ability to disambiguate the input with these factors\n",
            "\n",
            "====== GENERATION 4 ======\n",
            "This text is computer-generated. It is unique because the content was produced directly within a virtual environment. It was generated using techniques that have a long tradition in artificial intelligence, including visualization and speech recognition. This results in quality, well-formed text that conveys information with both clarity and structure. In this paper, we use a version of the system described in the first part of this article that is available as part of the GutenTag project (Hamp, 2010).2 This system creates scenes by visualizing a visual scene using the GutenTag visualizer. The GutenTag system is currently available from the Linguistic Data Consortium.3 The system will demonstrate novel techniques in the application to the natural language generation (NLG) community. These techniques enable our system to generate textual descriptions for spoken and multimodal examples. We don't present here an extensive evaluation of the generated content, except to stress what is possible. We show a prototype that is a small step toward achieving an ideal system with a system capable of generating natural language descriptions with clarity, good word sense disambiguation and minimal grammar. The rest of this paper is organized as follows. Section 2 presents our approach to generation. Section 3 outlines the generation component components and a prototype system that we are developing.\n",
            "In the past decade there was a large demand for an automated semantic lexicon for generation. Unfortunately the NLG community to produce a wide range of grammars from semantic databases and from a range of syntactic parsers. There is no single semantic interpretation formalism that can account for full semantic interpretation and semantics in any detail. Thus a variety of choices must be taken at different levels of granularity—semantically and morphologically motivated, lexically motivated, syntactic and lexically motivated, syntactic—and finally pragmatically motivated. This paper explores the impact of different choices in a semantic-level model.1 While linguistic representations have a potential to provide a common formal basis for multiple representations, there are no general solutions that apply one or another representation to different levels of meaning. Our hope is to avoid this limitation by combining representations into one or more representations that capture the semantics at all levels. Section 2 presents two approaches to representing meanings in a semantic level. Two approaches to syntactic analysis are presented in this paper. One is a bottom-up approach, in which syntax is interpreted as surface syntactic analysis. A second approach is a top-down approach, in which semantics is assumed to be based on semantics based on composition. We compare these representations: a bottom up approach where syntax is interpreted as a top down approach, and a top down approach where semantics is interpreted as a bottom up approach. The second model reflects the current state of the art in NLG—that we believe is the closest currently available—and contrasts itself to the first model. Our goal is to demonstrate a robust, complete, flexible, general-purpose generation system that is capable of producing fully abstract sentences (both written and verbal) from these representations. Section 3 describes how semantic representations, particularly the ones derived from a treebank, affect how language generation as a whole. It also discusses the utility of representation-neutralness within a language—the idea that a representation should be easy to interpret (and easy to produce) to a user. We present several strategies for achieving this goal. Our contribution is twofold: first, we use the output of a bottom-up top-down representation (to provide a consistent representation) that allows us to generate all possible interpretations, and second, we make representations easy to understand while generating. The first two sections discuss these approaches, whereas the third section discusses issues of composition. The paper gives an overview of syntax and semantics and a semantic representation within a bottom-up approach\n",
            "\n",
            "====== GENERATION 5 ======\n",
            "This text is computer-generated. It is unique because it was written by an unknown author, using an unknown language, and using an unknown software system. We cannot readily tell either who, what, and when the author is involved, or what the author’s goals are. It is the first part of a much longer text in which the author is opaque, but we can make some educated guesses about it given the limited vocabulary he used (e.g.. he may know about technology). Since we want to extract information from this text for a variety of applications, it should be of interest to researchers. The paper is organized as follows. In Section 2 there is an overview of related works. Section 3 presents the task of identifying anonymous essays. Section 4 describes the tools used. Section 5 describes the evaluation results. The last sections describe related work on the task.\n",
            "The growing amount of social media posts on Twitter, as well as its huge growth over the past decades, are forcing us to explore our communication and social media technologies to gather information more effectively. As tweets contain an increasing amount of information, we have a fundamental need to understand those micro-blogs and social media posts relevant to a given event. In response to this need, many previous research areas have started developing dedicated tools that automatically acquire information relevant microblogs to the given topic from one or more microblogs. In addition, other research areas, such as identifying opinions, expressing an emotion, and creating news, have developed related tools for news to sentiment and emotions. This has helped in identifying news stories related to the given topic. Unfortunately, but don't sufficiently capture the information from Twitter microblogs. Our goal is to automatically extract information from microblogs, and develop a general-level technology to deal with multiple event descriptions. Most current work is based on the following two problems: 1) extract information from Twitter, which has been reported many times previously (Liu et al., 2012, 2010b); 2) identify topic candidates, which has not yet been reported; and 3) identify the event descriptions, which have not yet been reported. We will briefly describe these tasks, and propose a framework for creating a general-level text mining system. Our goal is to leverage the microblog information gathered from any microblog to obtain news related events, or news, and generate news on Twitter based on those extractions. This paper describes a tool that works with Twitter messages, identifies events related to the given topic to the given topic, and finds possible events that match the given topic. In the remainder of the paper we briefly describe the related work, in that section we describe the previous research problems. In section 3, we give an overview of the basic problems, in that we motivate our approach, review previous work and propose a method. In section 4 we describe the design for language modeling, in which we describe some of the design features used for identifying events, but don't explain what is required to handle multiple event reports from different microblogs. In section 5, we describe the experiments and results. In section 6 we summarise and discuss related.\n",
            "This paper presents a machine learning system, named ISTeam (Interactive Text Summarization via Online), that is able to extract and summarize information from a pool of tweets and sentences. We propose a new approach of extracting and synthesizing news from a large dataset with text generated by a system that automatically identifies event descriptions. We will be using this system together with our system for constructing a summarization system that uses the Twitter information from our existing summarization system in a novel way to identify relevant news. The system identifies a given a set of tweets from all tweets and generate extracts from this large dataset and extracts that are relevant to some topic; a given event.\n",
            "\n",
            "====== GENERATION 6 ======\n",
            "This text is computer-generated. It is unique because it is a corpus-trained corpus consisting of full documents annotated by native speakers with the goal of creating a more annotated corpus for language generation techniques.\n",
            "The task of topic generation is to determine the topic to which a given user object is being referred. Previous researches have focused on generating the best candidate topic via discourse analysis (Rehn, 2009; Nguyen, 2011; Wang and Weld, 2008; Nguyen et al., 2011) or selectional preference models (Clark and Curran, 2004). The latter have the advantage of making reference to all words in the language, but have the disadvantage that they cannot use richer context information. Most topic models have either no model with structural diversity in the data (e.g., one topic model that models content selection and the other that models how words related to each document), or they use a linear model-based model to represent each document. In this paper, we propose a new model that addresses the problem of multiple topics via a rich set of features including topic types and context information dimension. In particular, we model each document directly with a set of lexical and topic structure features. Our model-based model takes advantage of document content features that captures the structure of documents, but also the type of objects and objects themselves that occur within a document, making the model a model not limited to a particular sentence structure. This model type but rather a generative model to capture the context of documents. Using the ‘information rich’ features, the model learns how to cluster information in the document and can use these features in an efficient and modular manner to group it into coherent segments. This paper is organized as follows: The introduction is structured as follows: we present related work in topic modeling, Section 2. We discuss a set of document based aspects in Section 3. Sections 4 and 5. We define the task of topic modeling and introduces the new topic model we propose in Section 6. Section 7 details our models, which are composed of several levels of information at a time, and we discuss the evaluation we performed in Section 8. We conclude with the findings and our plans for future work in Section 9. Proceedings of ICSC 2015, pages 20–23, Denver, Colorado, June 5, 2015. 2015.\n",
            "Language generation (NLG) refers to the task of automatically generating the most likely topic and coherent text from a given set of generated content. While there has been a long history of approaches, especially that of speech act theory (Austin, 1957), that assume the input to a NLG system consists of spoken expressions, the results are quite limited. Instead, the most commonly used generation paradigms are dialogue act models (Stolcke, 2000; Friedman and Friedman, 1994), rulebased modelbased NLG systems (Stolcke and Shriberg, 1999), and incremental generation systems (van Gerdemann, 2012). We present a machine learning approach to this problem by explicitly modeling the dialogue act recognition process as a multi-step process of word selection and sentence planning. Our results in generating more coherent dialogues with high inter-sentential informativeness with less errors on a large task-labeled corpus. Section 2.\n",
            "Language generation is a critical component for many application environments, including automated web content selection (Dolce et al., 2013) and human-robot interaction (Bernardi et al., 2011). The dialogue system for the DARPA GALE project is situated in a multimodal environment using a simulated physical agent that performs content selection (McEvan et al., 2014), and multimodal document selection (Jurcic et al., 2013). In this paper, we propose a computational model for automatic language\n",
            "\n",
            "====== GENERATION 7 ======\n",
            "This text is computer-generated. It is unique because of the use of a controlled, computer-generated style. Text generation was not addressed until quite recently in its pure form. However, many text generation systems are implemented with natural language techniques to produce natural-sounding output. There are two primary reasons for the use of natural language techniques to realize written language summarization. First, the style has distinct advantages in that it is less rigid than conventional text formatting. For instance, while a system needs to preserve the flow of information in order to avoid lengthy monologue, sentences are rarely reduced in length. A sentence can be longer to convey a particular meaning than other sentences. The second advantage is that natural language generation is more flexible in combining techniques for creating a concise natural language into a text. Since content generation is inherently procedural, it includes many complex generation techniques, such as referring expression generation (Geist, 1995; Hovy 1991), surface realization (Carpenter 1992), text generation (Reiter 1994), lexical cohesion and ellipsis resolution (Allen, 2000), and so forth. An individual generator may use many of these techniques to produce a certain effect. Generating a large body of text with such systems is time-consuming and often cumbersome. However, it is possible in practice to generate all of them with the same ease. This paper. We use here a generator known as the CUE system (Closest Approach) to generating textual summaries using simple natural language techniques. The purpose of this paper is to demonstrate the CUE system through a series of experiments involving both a human-generated and a natural language generation system in two different domains. We compare its automatic generation with manual and automatic approaches. We analyze results obtained on a collection of newspaper articles from Reuters' Newsgroup, showing that humangenerated summaries are more comprehensible (without being overwhelming) and easier to understand than synthetic summaries. We conclude by comparing our data with an expert opinion summary and a simulated evaluation of human-generated summaries.\n",
            "This paper explores the use of the word expert classifier for sentence compression using a multidocument summarization system developed under the DARPA BOLT program at Johns Hopkins University. The program, which has now been renamed SUMMA, reduces the redundancy of humanwritten sentences while keeping their content comprehensible. Summarization systems that use expert linguistic knowledge to determine what to say are based on the assumption that the sentence is already well written — that is, it contains the essential components of knowledge. The remainder of this paper will focus on the use of the word expert classifier for sentence compression in summarizing a large corpus: the use of the grammatical knowledge contained within a sentence. The system we consider is one of those developed for news summarization, and it reduces human-written sentences of the DARPA BOLT-1 program (Garfield 1996). The goal is to evaluate the contribution of such a system to the use of natural language techniques for summarization. A similar compression system, called HILDA (Levelt and Kelter 1999) was developed at AT&amp;T Bell Laboratories by considering two main areas of grammatical knowledge: syntactic coverage and lexical and sentence structure.\n",
            "In this paper we examine the use of lexical information to determine the content of natural language summaries. Specifically, we consider the input texts of the DARPA BOLT programs using the Noun Phrase Recognizer (Neal, 1998). Lexical structure is the part of the sentence that best captures the content to be included in a summary. It is necessary for summaries to be compact. Lexical structure is primarily used to describe the linguistic structure of the text, including syntactic constructions such as sentence order, syntactic analysis, and\n",
            "\n",
            "====== GENERATION 8 ======\n",
            "This text is computer-generated. It is unique because the authors and illustrators of the graphic had no prior knowledge of political science to begin with. Therefore, some of the work presented here was borrowed from that of the political scientists. The fact that the same ideas were used to present different results, however, suggests that the two fields might be heading in different directions. We propose two statistical models to describe in detail the two political parties in Spain and to explore the factors which combine as a cause for changing the parties’ positioning. We describe them both here in greater detail. In section 2, we describe a corpus of political party messages from a recent parliamentary meeting. In section 3 and 4, we describe a simulation experiments that examine whether the resulting results can be combined with a variety of linguistic and visual features for automatic prediction. Section 5 provides a detailed account of experimental results, and provides discussion on implications. We conclude.\n",
            "The term “sentiment analysis” is commonly used to refer to the classification of opinion words into 1. positive/negative, 1-positive/negative, or neutral/negative. Such an analysis can thus be seen as part of the field of sentiment analysis, where we attempt to recognize if an expression is opinion, a subject, a proposition, a sentence, or a sentence-clause or a sentence-sentence. It is quite common for a sentence to use the term “negative” to refer to the set of elements to which the subjective character of a sentence is to be directed. In our application, sentiment analysis is concerned with the analysis of a sentence, or sentence, as a set of words describing the subjective, the subjective intent of writers. Although this may be less relevant in opinion mining given that we often want the analysis of a phrase rather than a single sentence: one of such phrases. The other approach is to use sentiment words within a sentence in order to make explicit what the sentiment is, i.e., what the sentiment is about. Although the word can convey, it is nevertheless a relevant factor in the analysis of a sentence. If phrases such as “horizontalism” (positive/negative) could be argued to be expressions of same sentiment; at the same time, the sentences and words may not be identical: it may be not be evident that there is a difference in sentiment or its orientation. Thus the assumption with which we make is that a phrase such as “horizontalism” cannot necessarily describe the same underlying attitude. As for example the word polar’ of “negative” we have in common, we want to identify the attitude of the speaker towards the sentiment; this is not trivial. In other words, we want to identify the sentence in which the sentence is taken, so we cannot use it to say that the utterance is a negative. We assume that sentiment carries information about the position of the speaker: we cannot assume that both “negative’ and ” are independent elements. It is not a trivial matter to identify the polarity of a word: it is a property of the subjective element in the sentence that determines how it is used. The aim of this study may seem rather trivial to make use of a sentence that is subjective; for example, “paraphrased by” is the same phrase as “negative” but, of course, some form of subjectiveness may be required in order to identify the attitude of the speaker: “negative”, which, as we will see, makes the analysis of a particular sentence more complex. A similar to sentiment analysis to that of “negative”. In the case of ‘paraphrased by” a sentence, we want to understand the\n",
            "\n",
            "====== GENERATION 9 ======\n",
            "This text is computer-generated. It is unique because of the use of the LINGO grammar. The results of this grammar, as described in detail in the book and used for the production of this paper are intended for general linguistic analysis, not for generation. I could not agree more. The reader who has had at least a passing familiarity with string theory will find the work here a rich source of ideas. One of the goals of this book is to demonstrate the usefulness of the ideas presented. However, to demonstrate its utility we have chosen examples from actual surface realizations. The ideas presented in this book are well known and discussed in the leading grammars and related disciplines. I hope that the reader will find them helpful as well. I would like to thank Douglas Appelt who provided me with his detailed descriptions of the system and with useful examples of how to integrate the generator with other programs. Any feedback or comments would be greatly appreciated. I am grateful to Douglas Appelt, James Martin Kay, and the anonymous reviewers for their valuable constructive comments. The project described here would certainly benefit from a continuing presence at LINCAC. I have been more than satisfied with the amount of work done, and the amount of detail provided. This is in this book. The following three sections give a clear descriptions of current practice regarding the use of unification and finite state techniques in NLG, and present some research points I think must be added at some time. 2The book, \"E. Nederhof and E. 1991. Computational Linguistics and Programming Languages: Theory and Practice\", should not be regarded as representative of the wider research in this area. In particular, the book does not discuss generation more general grammar based approaches to linguistic analyses; however, some suggestions and points on certain basic grammar patterns are suggested there that relate to real problems. More generally, I wish to incorporate some lessons of computational linguistics and computational linguistics into this book. 2.1. 'The book's goal to introduce new techniques in both field has been realized through a series of exercises. The exercises consist of a series of descriptions of surface realizations of a large set of grammar examples from a given problem that involve a variety of approaches. Examples may be used to solve (some of the issues in the) problem in some different ways, and the results might be interesting for a wider audience. The exercises have been developed using the NLTK, the 'Natural Language Tools' program developed at the LINC ACRL. Each exercise consists of two parts, a presentation of a solution or a presentation of the solution itself. The problem is an open problem and the techniques developed technique are general. The presentation and solution itself is only a description of a method. There are no assumptions about the problem itself. The purpose of the exercises is to motivate the techniques. What this book does do is to demonstrate those techniques to general readers and provide a set of examples to demonstrate them. The exercises are not presented to give the reader any specific theoretical justification for the techniques. There exist exercises in the LINC program that were not used in actual generation, or were used but in some other manner.\n",
            "Most studies in NLG in recent years are centered on \"toy\" problems where the goal is to generate texts from information on a set of (some kinds of) input. The output. The type of generation problem that is discussed in that context is discussed is one in which the \"toy\" problem is a natural and straightforward one that is easy to understand. An NLG. Examples of the types of examples generated are a simple question, an \"is in fact a fact\" and so much of a more like other NLG that they are similar to more conventional techniques than they appear at first sight.\n",
            "\n",
            "====== GENERATION 10 ======\n",
            "This text is computer-generated. It is unique because in addition to the standard, more typical, graphics output that many of us have come to expect from the mainstream of our daily work, the system is a demonstration of the application of a fully formal and carefully engineered graphical interface whose potential is clearly limited to the very narrow field in which it can be applied. The first paragraph of this paper demonstrates this point with examples from the Switchboard corpus (Woods 1979), where the language described by the word tapes in Figure 2 is being converted to a \"graphemic alphabet\" capable of representing the phonemic sound-list of about 2 dozen sounds in addition to the eight letters that form the standard ASCII character set (ISO-Latin-3): (a) consonant accented; (b) stress tone accented; (c) stressed syllable accented; (d) stressed word accented; (e) voiced sound accented. (As input the word tapes are read from a standard keyboard in a \"command-and-control\" mode. As the grapheme encoder runs one step at a time, producing a stack of graphemic signs) Since input cannot be entered by a mouse or keypad, both written-out and pen input modes need to be manipulated. As input is restricted to a single word at a time and the graphemic encoder has to be used in combination with a menu, one can imagine an interaction that goes from graphemic signs to \"I\"s, o's, A's, and O's in response to mouse button-presses. The input modes also offer no option for users to interact using a mouse. Only a mouse can be used to scroll through the input mode. The next paragraph explains the choice of modes from which to output phoneme at a time: (a) grapheme-to-character; (b) grapheme-to-sound; (c) phoneme-to-sound; (d) spelling-to-text; (e) spell-to-text, and (f) pen-to-speech. The next four paragraphs describe the use of the system as a tool for providing a \"personal virtual assistant.\"\n",
            "The main advantages of a personal digital assistant are convenience (Dale, 1990) and the reduction of the dependence on a computer. We can use the telephone directly interactable system to communicate with other humans without going through a lengthy setup and without requiring substantial investments by the user. However, the system must maintain an interface. It must keep within the bounds of what was necessary to create the system described above. If it were to be used on an ordinary telephone or teletype keyboard, it could easily result in an \"all-fretz\" and/or unpadged utterance. This causes the user to lose the mental picture and will result in communication breakdowns. Furthermore, the user knows no word by word but the word sequences of the system. One solution at this point would be to use a combination of techniques for combining multiple word-to-word pairs. This solution seems reasonable, as text-to-speech systems don't seem too different from the spoken dialogue systems. It is important, however, to keep these units separate as close as possible, since the user will not remember how or even remember the names of the language. Another solution is to allow the system to recognize that the user does not have any words in mind during the communication. This solution does not seem natural but is not guaranteed either. A third solution may be given, e.g., for the user already had recognized but then to ask the system for a name when at an ordinary time. Since the language may be different, a natural approach\n",
            "\n",
            "====== GENERATION 11 ======\n",
            "This text is computer-generated. It is unique because we don’t have to build a separate human or machine dialogue component for each concept. 2. This dialogue system is more efficient than human-machine dialogue systems because it needs to deal with an utterance (or a series of utterances), rather than simply ignore it and wait for the next dialogue. 3. This dialogue system has the flexibility to ask questions about the domain, about the user’s interest, and about the state of the system. The last part is important, since most of the research effort in developing the dialogue component for question answering and dialogue system has been focused on English questions. In this paper, we focus on the first two points and evaluate the first three aspects. Using a corpus of naturally occurring texts from a medical domain, we examine the effectiveness of our proposed dialogue system on answering a wide variety of commonly-used medical domain questions and determine the factors that affect the effectiveness of our dialogue system. Section 1 illustrates our system architecture by describing several types of dialogue actants in the medical domain. Section 2 describes how our dialogue system compares to several human-machine dialogue models. Section 3 introduces a domain adaptation technique that is applied to the dialogue system architecture to improve its performance. Section 4 evaluates our results. Section 5 proposes a new domain adaptation technique to deal with question answering. Section 6 and Section 7 discusses the importance of domain adaptation, while section 8 concludes the paper.\n",
            "The increasing amount of electronic health records make it necessary to deal with large volumes of patient record. An important task in health care management is that deals with patient care management from multiple angles, ranging from providing patient care to maintaining effective patient care. There is an overall patient management environment. When considering the problem of maintaining patient records, one concerns at what percentage of the records are really necessary for the clinical goal, what effect these records do, and how important it is to preserve them. Because the problem of maintaining a patient health care environment is such a difficult problem, a good and detailed study of patient care should take a look at this issue from all angles. Medical record management is obviously a complex problem. Therefore, research in general medicine must take into consideration all aspects of record management, including the following three factors: 1) the collection, preservation, and representation; 2) the format, formatting, the encoding, the retrieval, and access of the records; and 3) the selection and organization of the materials assigned to the records. The medical field has become increasingly complicated and the amount of time taken to transcribe a large portion of the record keeps rising to the point where many have become obsolete or very repetitive: an order of magnitude must not be taken to keep up with what is required to keep up with the needs of all the needs. A few attempts have been made to organize medical records according to their nature, which can be divided into five areas: (a) physical documents, (b) natural language, (c) formal language, (d) abstracts, and (e), for general information retrieval by topic (f) computer, e) text and speech, and (g) speech. However, none address the need for the most important type of record (g) medical document: the problem of indexing the medical record. It is in many ways challenging to determine precisely what is most important. In order for a doctor to make a decision, he or she must look at all the documents; however, the information that has already been transcribed must provide him or her with a notion from which he can draw initial, the next document. This requires the careful consideration of the whole context of the problem. Because of keeping the document to select from the various documents, there are a number of methods for selecting documents from\n",
            "\n",
            "====== GENERATION 12 ======\n",
            "This text is computer-generated. It is unique because of the use of lexical simplification rules that transform sentences into shorter and more coherent summaries; because we employ sentence simplification for summary generation and not for sentence compression; and because our summary generator relies on information structure in the input to select and generate candidate output sentences. We believe that this technique can be useful in situations where a computer system produces summarizations of very short conversations that reflect a high level of conversation. Such conversations tend to be low level, consisting of only a few turns on a variety of topics. Consider for example a chat transcript where the conversation is about a family trip. The conversation about the family trip, from a conversational history perspective, might be characterized by relatively brief (10 to 20 words) utterances about one or more topics. It ends with a discussion about one or more dinner items. This transcript can be a useful gold standard for evaluating the feasibility of our algorithm by comparing it to other related work. A second and complementary use of a conversational history for evaluating summarization techniques is to evaluate summaries of the content of conversations that contain short or mid-length conversations (such as the informal and casual nature of conversations that usually occur during a meeting). We know of no evidence that casual conversations are as informative as standard meetings. Therefore, this paper aims to establish the feasibility of a summary approach, based on a corpus composed of conversations and summaries that are shorter than five minutes (for which the length limitation of the dialog is a predictor). We don't intend to create a model for a model for content selection—sentence compression or summary generation—as in compression—which we intend to apply just to conversations. A natural approach to this problem, a mixture of conversational history and human-authored output, is an idea that deserves more attention. There are two basic questions that we must ponder: • What is the effect of the length of the chat and the length of the sentences? If a high-level analysis will reveal the role of language in generating the topic of the conversation weblog for a conversation. • How much of the message of the conversations (text) is in the conversational history? If the conversational history is of a sufficient size, what is the effect of the length of the sentences? In what sense should this corpus be useful for evaluation? • What would the length of the chat and the sentences be useful for evaluation? We begin by asking these questions by asking the following questions: 1) Do conversations contain any of the common vocabulary items in common conversation? 2) To what extent the utterance seems natural to the target reader? 3) How much of the utterance is likely to contain syntactic information? From these points of view, we can make some assumptions about the length of the chat and the length of the sentences: length of the conversational history. Section 2 of this paper presents examples of the use of text and the length of the dialogue as a predictor of speech and syntactic structure of the weblog. We start by presenting the context of our work, namely the conversational histories of the participants of a conversation. Section 3 lists sentences that don't contain any (human-generated) data; Section 4 describes our corpus as well as the corpus itself; and Section 5 presents our evaluation. We show the results of our evaluation in Section 6. The remainder of the paper is organized as follows. We first describe our evaluation methodology. Section 3 describes the task, details an evaluation protocol for automatic content selection from common (monotonic) text, and introduces the text simplification of sentences. Section 5-based approach to summary generation. In Section 3, we analyze the effect of data complexity and of the chat and sentences. Section 5. Our approach is\n",
            "\n",
            "====== GENERATION 13 ======\n",
            "This text is computer-generated. It is unique because its writing style is stylistically and phonologically distinct from other media of the same genre. While it is a work in progress and incomplete, we believe it to be a valuable resource for theoretical and practical linguists and language technologists who wish to explore the stylistics and phonological variation of certain languages. It is a faithful faithful replication of the text of the early 20th century literary critic John Hughes, who wrote: There is no literature which stands above all other literature; there is only literature which is itself a literature. (Hughes, 1920) We are grateful to the authors of this article: Kathleen McCoy (University of North Carolina at Greensboro, USA) Barbara Grosz (Charles University, Czech Republic) Richard Penninelli (Rutgers University, US) Kathleen McCoy (University of North Carolina at Greensboro, USA) Barbara Grosz (Charles University, Czech Republic) Richard Penninelli (Rutgers University, US) Asher Rens Bod (University of Aberdeen, UK) Paul Buitelaar (University of Lancaster, UK) and the project committee. We especially thank our SIGANN board and the program committee, and reviewers for their help in reviewing the submitted papers. We wish to thank our authors and the anonymous reviewers as well. Sincerely, the SIGANN board and the project committee\n",
            "The aim to develop a multilingual text-to-text system capable of providing access to information over the Internet. To this end the input of a computer system is processed in a language independent manner. The input is entered into the machine by presenting the output of the computer system in form. This input and output constitute a single string of words. Outputs are interpreted by applying the natural language analysis to the output of the machine. A lexicon of the machine itself and a grammar of the machine are used to provide a representation of the text. The text-to-text system has the following goals: To facilitate the text presentation for translators and the readers, to provide a unified presentation for the readers on complex phenomena of particular languages which cannot be expressed by the conventional way. Thus, the text should be a natural language and the output should be well organized and well structured. As mentioned, the output must serve as a representation of the meaning of the text and provide a representation of its logical form.' Thus, the output structure should be based on a well designed formal approach and present at a level of coherency that minimizes the number of inconsistencies between the output and the input string as much as possible. This means that the output should be understandable to a language reader with some degree of competence.\n",
            "The use of machine translation to convey information has been gaining in popularity in recent years. The goal is to facilitate the access to information from texts that cannot be conveyed in another language. Examples include translating machine-readable documents (e.g. technical manuals, computer manuals or encyclopedia texts) to machine-readable text (e.g. news or newspapers), or producing texts output by computer to machine (e.g. electronic dictionaries and grammars). Machine translation (MT) is usually used to provide this access to information in the form of multilingual corpora of documents written in several languages. The most known MT systems is: (1) by bilingual dictionaries, like dictionary, (2) bilingual dictionaries of machine-readable documents, and (3) by a bilingual corpora, such as corpora of English and French. The aim of this paper is to present, that is to present the first attempts at building MT systems capable of translating text from machinereadable to machinereadable form and to natural language: In particular, we study English, German, Dutch, Japanese\n",
            "\n",
            "====== GENERATION 14 ======\n",
            "This text is computer-generated. It is unique because it was produced by a group within a single company. The structure of the document is rather simple, and it presents the facts in English and Spanish, using a fairly standard machine-script: first, in boldface, the main conclusion of a newspaper report is presented; next, the sentences describe a variety of different research; finally, a group discusses key issues and their implications for the company. The purpose of the document is to provide readers with a well-defined view of the company itself, and thus its potential connections to the next major event in the company's development. The original documents were submitted to different newspapers: the Associated Press (2,000,000 stories), Associated Press (2,300,000), Los Angeles Times (1,500,000), Wall Street Journal (1,200,000), and Columbus (150,000). The total number of stories is the second highest. Because the information was drawn from the company's general knowledge sources, there are some inaccuracies; moreover readers should not assume everything that the authors have said. Readership attribution is a solved problem. An attempt has been made to identify the authors by using statistical techniques. The authors will be called, from a list of titles and titles. Readers will be called by their first names, if applicable, if not. Readers should be assured that the data is representative in the company's domain. To keep readers from having the wrong idea, the data is typewritten and that way. The paper is not typewritten, its authors were asked to write, without special techniques, such as a computer program that scans one paragraph at a time from a single page, or a keyword system. (The figures presented here are taken from newspaper articles, and are from that paper. The results are not necessarily representative.) 1.0 the papers reported, are from the AP, and show a similar style to the original, not necessarily in the same form. The text is computer-generated. Readers should look at the original for more than the summary style, but rather as a representation of the contents in a knowledge base. The style of a text is an important feature in identifying it. We are not assuming that each sentence is representative of the original. There were several factors involved: readability; vocabulary; style: the sentence is short; the style is familiar; the vocabulary and jargon; the language is English; the length is very short. The length of the material is not too short, and the authors’ knowledge of the company and its relations to the next major event is not overwhelming. A sample of the original: (1) a synopsis of an American newspaper and company’s latest, and the changes that have made it; (2) a company’s report on the same issue of the same company in the mid-1960s from the 1950s, showing major changes in the company and its future; and (3) a newspaper’s report on how the company made a profit at the same time as the same year. These four papers are the only published sample of the type. The goal of the method is to identify the authors by their writing, so readers should not rely on the results, which are not known.\n",
            "To enable computers access to document collections containing descriptions of the environment they were assigned by natural language search engines, or to automatically retrieve the information contained descriptions of natural language documents, is an important problem of natural language processing. A natural language search or browsing, is a critical capability that should not be ignored if the users have the knowledge to browse document collections; in part, this issue, the size of the document collection and the information presented to a query is the major factor determining the accuracy of searching\n",
            "\n",
            "====== GENERATION 15 ======\n",
            "This text is computer-generated. It is unique because we have applied a machine learning algorithm for the automatic generation of conversational speech that simultaneously avoids the extreme imbalance of spoken language with text. By using machine learning technology to infer a user’s language, we can successfully simulate a highly conversational, multi-party conversation. In addition to this paper, we are planning a workshop at the University of Michigan in June, which will be part of a more general NLG research program at the University. The workshop will run seven hours per session, seven days a week. We anticipate that our workshop will draw a diverse group of interested researchers from around the world to this special event. We would like to thank all of the authors who submitted papers for their excellent work. A special thank you also goes to the faculty advisors who helped in planning this and many other great workshops, as well as the authors, reviewers and the presenters of this papers for making this year’s workshop a great success. We thank the SIGDIAL board for allowing us to hold this special workshop. Finally, we thank all the individuals whose writings and reviews helped make this workshop possible. This year’s workshop served as a first opportunity to give back to the field of natural language generation through a workshop. More than 1,000 program hopefuls applied to their work in previous years. So far, nearly 350 people have submitted a paper and received a paper accepted for oral presentation and accepted as paper. The workshop was always successful, and this year’s workshop is an opportunity for all students to present their work and receive feedback and suggestions as they are submitting. We hope in the future to foster this spirit. The workshop will continue to be a rewarding one. We are indebted to the members of the program committee for providing helpful advice and assistance as well as reviewing every submission. We would like to thank the workshop organizers, the program committee and the participants and everyone who showed interest, all of whom are already familiar with natural language generation. As of August, the workshop has been held in conjunction with ACL-IJCNLP 2015 and ACL-2015. Some local arrangements are under way for future events. We anticipate all future events to be held in conjunction with ACL-IJCNLP 2015. We hope that this year’s workshop will have an even larger influence on the development of future AAC research. We thank ACL-IJCNLP board members: Marilyn Walker, Beth Ann Bies, David Children, Chris Finin, Dan Roth and Brian Roark. The ACL SIGDIAL board members: Bonnie Webber, Bill Byrne, David Chiang, Carolyn Pennebaker, Marilyn Walker, Mark Dras, Lisa Lise Getoor, Jason Smith, Mitch Marcus Martin, Mitch Marcus, Marianne Santaholma, Peter Schutze, James Allen, Amanda Stacey Rosenschein, Jennifer Sparck Jones and Marilyn Walker. Special thanks to the authors and conference hosts for an excellent conference, ACL-IJCNLP-2015 and ACL-2014: Bonnie Webber, Beth Ann Bies, Daniel Bies, Daniel Dras, Dan Roth and Brian Roark. The ACL SIGDIAL 2014 General Chair: Kristy Boyer, ACL-Johnson Chair: Kristy Boyer, ACL-Johnson Annette Bies, Ann Bies, Rebecca Hwa, Carolyn Pennebaker, Douglas Hwa, Ann Bies, Dan Roth, Mark Dras, Kathleen Raghavan, Marianne Santaholma, Martin Kay and Beth Ann Hockey. Program Committee: Amy Adler, ACL Publications: Carolyn Rose, ACL-IJCNLP -Workshop-Co-C/S, ACL-IJCNLP-2014-2014\n",
            "\n",
            "====== GENERATION 16 ======\n",
            "This text is computer-generated. It is unique because it was created by an individual, based on an interpretation of the situation that it describes, the knowledge that it uses to interpret it, and the linguistic resources that it employs to generate it. For me this interpretation comes from the fact that any discourse system that uses words as meaning in addition to their syntactic function must take into account the role played by pragmatic and affective information in describing information from the knowledge sources in the sentence. We call this knowledge the information status of anaphors while making some specific claim about linguistic meaning, including for example the proposition that one sentence can be meaningful as a meaningful sequence of sentences, or that meaning is best expressed by a sequence of sentences. All of this makes this paper more than an editorial. It tries to engage the reader in a process of reading to understand. While this task is rather complicated for a computer novice, it is very similar to the process that one might use to understand or make sense of novel texts, including poetry, stories, historical narratives, and so forth. In this paper, I propose that reading of a text becomes one of the last stages before the actual processing of a text. In reading is complete, the reader sees whether the meaning of a text in terms that reflect the meaning of the knowledge sources used, and if there is one (and if there is, how the meaning is expressed). In this paper, I suggest that a reader can learn something about a text by reading its components and their meaning, not by reading them independently. The remainder of this paper is organized as follows, starting with a discussion of four elements of reading, the organization of meaning into a set of meaning-representations, and the organization of anaphor occurrences into a sequence of meaning-representation forms. The next section discusses the process by which the reader learns what a reading of a text means. The next section presents the nature of the learning process that accounts for anaphora and the processes that use them in order to understand. The last section presents the process of understanding. I don't claim to present the theory that this process would be possible, and don't require a computer. The paper is meant to be read by anyone without the background knowledge of computer science. There is no problem with the idea that reading works when an understanding of text must have a connection to a knowledge base such as a syntactic or semantic analyzer. The purpose is to show the relevance of the representation that is used to communicate knowledge of the world, not to present the process that creates the knowledge. It is possible to have read-by effects using a syntactic analyzer to compute the meaning as a result of the representation, because it makes use of both the world and the reader.\n",
            "In the past few years a number of computational linguistic theories in natural language semantics have emerged. Each of these theories has received attention in its own right. In the framework developed and developed literature one can expect a variety of approaches to the analysis of natural language expressions. The major syntactic theories, such as those of Lexical-functional Grammar (Grimshaw 1993), Head Grammar (Hume), Head-Driven Phrase Structure (Kay, 1979), Generalized Phrase Structure (Pollard &amp; Sag 1987) (Chomsky 1986) and Functional Unification Grammar (Kay, 1985), Generalized Phrase Structure and Head-Driven Phrase Structure (Dikovsky 1988), Functional Unification Grammar (Kay, 1988), Topical Dependency Grammar (Zettlemoyer 1996) and Head Driven Phrase Structure (Kay, 1994), and Head Driven Phrase Theory (Kamp 1981) have achieved a certain degree of success and have generally made their way\n",
            "\n",
            "====== GENERATION 17 ======\n",
            "This text is computer-generated. It is unique because of the natural language processing components, such as natural language generation and lexical substitution. It has been built from over 130,000 computer-generated linguistic annotations. The annotations are applied and updated automatically to update the glossary and database. This has made it possible to use current linguistic techniques to make new glossaries, and extend our database of hand-corrected errors (Foth et al., 2010). These annotations, with many thousands of words removed and corrected over a period of 20 years, have enabled our research team to produce a corpus of over 250,000 word glossaries and error-annotated resources and a web-based tool that allows learners to experiment with various spelling and punctuation correction methods (Zielinska et al., 2013b). The remainder of the paper is structured as follows: In Section 2 we describe data that were collected while the project was active, describe related work, present our methodology for improving glossary and error annotation and our experiments. Section 3 describes the manual and automatic annotation, section 4 outlines the system for automatically annotated corpora and discusses evaluation.\n",
            "Language processing tools enable us to interact with digital information faster, less labor intensive ways and help us to find the information we need. Tools like WordFreak2 enable people to browse free text without entering keywords in large amounts. The tool is built in a web environment and is free for both non-commercial projects (commercial and enterprise). It also offers visual interface to help learn and visualize language at a glance through visual presentation of the lexicon along with a collection of associated tools and associated tools. Most current NLP tools that support web search require users to enter their queries, however, WordFreak supports a number of queries, not all of which are written in English. These are: (i1) a query-by-query system and (ii) a one-page system that takes users from one language to another by means of a virtual keyboard interface (iii) a user interface to help to search for information, visually and (iv) a graphical search by keyword/phrase-based retrieval and (v) a word search to look up content. The remainder of the paper is divided as follows. In Section 2 we present a description of the software and in Section 3 we present the search functionality. Section 4 describes the visual interface. In Section 5 we discuss the evaluation. We conclude with a conclusion in Section 6.\n",
            "Grammatical error detection and correction, sometimes also known as spelling correction of word in context, refers to finding the word (or phrases) that are being used in incorrect contexts and hence make an inappropriate grammar. Grammatical errors are, in fact, difficult and involve a process whose results can be difficult to evaluate. The task of grammatical error detection is closely related to grammar, and is related to and treated in a very similar way. Both are problems with many issues, however, grammar and grammatical error detection is even more closely related to the errors in spelling for both can be considered to share some issues (Kittredge, 1979). Grammatical errors are often considered to be problems that have a direct relation to grammar, (see for example (Lehrer and Johnson, 1994), (Pereira and Ishara, 1986) and (Brown and the results of this work), and (Kittredge, 1979) and more recently a lexical error which is used to deal with a form error of the error in English (Nagata, 1991). Grammatical error detection and correction is thus much like the error detection and correction of a word stem or word form. While there are many reasons for the use of such grammatical error detection in an NLP tasks such as corpus\n",
            "\n",
            "====== GENERATION 18 ======\n",
            "This text is computer-generated. It is unique because it is the written English of the president of the Philippines, Grace Period (1964-2009). It is the official speech of the Philippines, the first written Philippine constitution and was the basis for elections held in 1998. In 1996, the People Power Act put people power back in the spotlight, and the rise of multi-party dialogues began to attract attention in the international community. However, people power is a complex phenomenon, and it often fails to achieve good policy performance. As a result, many public works projects have focused on creating systems that learn policies from user responses, which is why we developed NOMAD (Natural Language Question Answering with a Knowledge Bases). NOMAD has two main functions. When a user is using the program, it helps to recognize the system’s utterances and answers these queries in natural language. Other uses is to help other people by communicating with the system and obtaining more answers from them in other language. Since theses uses are quite different, we don't address these two activities together. In this paper we argue that an accurate information state tracking system can help the people. Such a system helps to perform a number of tasks that are: (1) helping users to recognize possible problems, (2) providing new tasks to the user, and (3) helping to learn about the state transition from one state to another. To achieve these tasks, we propose two state tracking applications, named Task 1 and Task 2, which use machine learning techniques to perform state trackers. In Task 1, for example, it learns a number of features and then matches these features with the user utterances to create a state transition model. In Task 2, a trained predictor maps the user utterance to the state transition for the task. The learner also carries out dialogue actions for the learner and the user to perform in Task 1. All these actions are carried out in a cooperative manner across the two tasks. We use data to represent the user utterances, the system, and the expert knowledge and the utterances, and introduce a new way of learning dialogues. Since it is natural language, the goal of each task is the same: help the user through a response to the user's question and a new task to the expert knowledge. In this paper we have chosen the dialogue strategies for these tasks, in which the system acts as a user’s agent rather than the user. The basic task is to determine the correct target state (or goal) of the interaction, with the expert knowledge. The problem is defined as follows: Given a user utterance, NOMAD looks up its training data and predicts state change in the goal state. In this paper, we use data that we collected from several users who have signed up to participate in the NOMAD dialogues. The system consists of a speaker, a system (a natural language processing component) and several expert knowledge. The grammar used in Task 1 and Task 2 is the same as the ones used for Task 1 and Task 2. We use a two-level grammar, used for Task 2, but we adopt a two-level scheme since it is a two-level system. Our intuition is: in solving the problem of learning the system, the user’s utterances and expert knowledge are the same and one can find several possibilities in different positions of the target state. The problem is to find a given set of possible moves with the aid of the user’s words. The system that can help the user to achieve the target state transition. In Task 2, the user is the agent. All this is a two-level problem. We only uses a large amount of data, not necessarily\n",
            "\n",
            "====== GENERATION 19 ======\n",
            "This text is computer-generated. It is unique because it has stood the test of time, and was written without sophisticated computer tools. There are other genres that have seen the light but also, perhaps predictably, not in this form. Traditional literary texts, including newspaper and magazine articles, lectures, and books, are increasingly electronic. More importantly, their style has changed significantly over the past fifty years, from free phrase-and-styl to controlled vocabulary and rigid sentence structure. There is a general tendency in literary theory to assume that some stylistic phenomena manifest themselves through the use of a variety of stylistic devices, e.g., substitution, reduction, and ellipsis. However, the fact that the style of a text is typically not determined from its content (and how much content is in-use) does not reflect any principled methodology allowing for the reconstruction of stylistic conventions from text corpora. We will describe some of these constructs as well as a range of ways of characterizing the text from different perspectives. There are various ways of making the text more accessible. One way of characterizing a text is by characterizing its communicative act. For the sake of brevity we shall not attempt to provide an exhaustive characterization, but rather propose a dozen or so basic categories for each topic. An initial basic ideas that would serve as a starting point in defining literary style and illustrate their applicability to an author's literary work. These categories.\n",
            "There are many ways of writing, such as prose, dialogue, and essay, of expressing concepts in simple-verbal terms. These categories expressing concepts in complex-verbal categories (e.g. nouns, verbs, adjectives, adverbs, and adverbs. For the more sophisticated, the categories include concepts such categories as 'conceptive, superconceptual, and descriptive). A large variety of different writing approaches have been discussed in the fields of discourse and pragmatics, from literary to formal to descriptive. The simplest is the most well studied, and is an approach to communication between two or more individuals with the task of communicating about an entity, such as a person who is talking about himself. More sophisticated approaches use techniques such as visualization of graphs and plan-based communication such as the use of a computer program. These are clearly an area of interest to both authors and writers. They are used both for content analysis and for explanation. The first step is a description of the text to be communicated. A description of the text must be written, which contains the basic concepts expressed by the concepts. The task of specifying the text is accomplished by constructing an encoding of the text, which is then composed of information about the information. This information must be presented in a format which can be visually displayed (e.g. using a graphics program). If there is an element which can be used as a key to access the encoding, we might refer to an object as key. The purpose of an encoding is to facilitate communication by making it easy to follow the instruction. If the encoding takes on the form of text as displayed, these principles must be followed. One aspect of the encoding is the use of a text that is able to perform all of the processes which are necessary to communicate the message. The output of the encoding is a written text. The next step is to produce an encoding which provides a surface form of communication as it is received. This step requires text planning which can be used for purposes other than the one of encoding. The problem of this step is the design of a text. It is difficult to say anything about the encoding in detail that can be used by a computer program. It can be used by a human reader while also performing tasks other than just looking and speaking. The approach, although it may seem trivial\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}