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ÚFAL LatinPipe at EvaLatin 2024: Morphosyntactic Analysis of Latin

This repository contains the LatinPipe parser implementation described in the ÚFAL LatinPipe at EvaLatin 2024: Morphosyntactic Analysis of Latin paper.

📢 Besides this source code and the trained model, LatinPipe is also available in the UDPipe LINDAT/CLARIN service and can be used either in a web form or through a REST service.

LatinPipe Architecture

ÚFAL LatinPipe at EvaLatin 2024: Morphosyntactic Analysis of Latin

Milan Straka and Jana Straková and Federica Gamba
Charles University
Faculty of Mathematics and Physics
Institute of Formal and Applied Lingustics
Malostranské nám. 25, Prague, Czech Republic

Abstract: We present LatinPipe, the winning submission to the EvaLatin 2024 Dependency Parsing shared task. Our system consists of a fine-tuned concatenation of base and large pre-trained LMs, with a dot-product attention head for parsing and softmax classification heads for morphology to jointly learn both dependency parsing and morphological analysis. It is trained by sampling from seven publicly available Latin corpora, utilizing additional harmonization of annotations to achieve a more unified annotation style. Before fine-tuning, we train the system for a few initial epochs with frozen weights. We also add additional local relative contextualization by stacking the BiLSTM layers on top of the Transformer(s). Finally, we ensemble output probability distributions from seven randomly instantiated networks for the final submission. The code is available at https://github.com/ufal/evalatin2024-latinpipe.

Content of this Repository

  • The directory data is for all the required data (UD 2.13 data, harmonized PROIEL, Sabellicus, Archimedes Latinus, EvaLatin 2024 test data).

    • The script data/fetch_data.sh downloads and extracts all the data: 
      (cd data && sh fetch_data.sh)

  • The latinpipe_evalatin24.py is the LatinPipe EvaLatin24 source.

    • It depends on latinpipe_evalatin24_eval.py, which is a modularized version of the official evaluation script.

  • The latinpipe_evalatin24_server.py is a REST server with UDPipe-2-compatible API, using latinpipe_evalatin24.py to perform tagging and parsing.

The Released latinpipe-evalatin24-240520 Model

The latinpipe-evalatin24-240520 is a PhilBerta-based model for tagging, lemmatization, and dependency parsing of Latin, based on the winning entry to the EvaLatin 2024 https://circse.github.io/LT4HALA/2024/EvaLatin shared task. It is released at https://hdl.handle.net/11234/1-5671 under the CC BY-NC-SA 4.0 license.

The model is also available in the UDPipe LINDAT/CLARIN service and can be used either in a web form or through a REST service.

See the latinpipe-evalatin24-240520 directory for the download link, the model performance, and additional information.

Training a Model

To train a model on all data, you should

  1. run the data/fetch_data.sh script to download all required data,

  2. create a Python environments with the packages listed in requirements.txt,

  3. train the model itself using the latinpipe_evalatin24.py script.

    To train a model performing UPOS/UFeats tagging, lemmatization, and dependency parsing, we use

    la_ud213_all="la_ittb la_llct la_perseus la_proiel la_udante"
la_other="la_archimedes la_sabellicus"
transformer="bowphs/PhilBerta"  # or bowphs/LaBerta

latinpipe_evalatin24.py $(for split in dev test train; do echo --$split; for tb in $la_ud213_all; do [ $tb-$split = la_proiel-train ] && tb=la_proielh; echo data/$tb/$tb-ud-$split.conllu; done; done) $(for tb in $la_other; do echo data/$tb/$tb-train.conllu; done) --transformers $transformer --epochs=30 --exp=evalatin24_model --subword_combination=last --epochs_frozen=10 --batch_size=64 --save_checkpoint

Predicting with a Trained Model

To predict with a trained model, you can use the following command:

latinpipe_evalatin24.py --load evalatin24_model/model.weights.h5 --exp target_directory --test input1.conllu input2.conllu
  • the outputs are generated in the target directory, with a .predicted.conllu suffix;
  • if you want to also evaluate the predicted files, you can use --dev option instead of --test.

Contact

Milan Straka: straka@ufal.mff.cuni.cz
Jana Straková: strakova@ufal.mff.cuni.cz
Federica Gamba: gamba@ufal.mff.cuni.cz

How to Cite

@inproceedings{straka-etal-2024-ufal,
    title = "{{\'U}FAL} {L}atin{P}ipe at {E}va{L}atin 2024: Morphosyntactic Analysis of {L}atin",
    author = "Straka, Milan  and Strakov{\'a}, Jana  and Gamba, Federica",
    editor = "Sprugnoli, Rachele  and Passarotti, Marco",
    booktitle = "Proceedings of the Third Workshop on Language Technologies for Historical and Ancient Languages (LT4HALA) @ LREC-COLING-2024",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.lt4hala-1.24",
    pages = "207--214"
}