| # 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](https://hdl.handle.net/11234/1-5671), |
| LatinPipe is also available in the [UDPipe LINDAT/CLARIN service](http://lindat.mff.cuni.cz/services/udpipe/) |
| and can be used either in a web form or through a REST service. |
|
|
| --- |
|
|
| <img src="figures/LatinPipe.svg" alt="LatinPipe Architecture" align="right" style="width: 45%"> |
|
|
| <h3 align="center"><a href="https://aclanthology.org/2024.lt4hala-1.24/">ÚFAL LatinPipe at EvaLatin 2024: Morphosyntactic Analysis of Latin</a></h3> |
|
|
| <p align="center"> |
| <b>Milan Straka</b> and <b>Jana Straková</b> and <b>Federica Gamba</b><br> |
| Charles University<br> |
| Faculty of Mathematics and Physics<br> |
| Institute of Formal and Applied Lingustics<br> |
| Malostranské nám. 25, Prague, Czech Republic |
| </p> |
|
|
| **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 <a href="https://github.com/ufal/evalatin2024-latinpipe">https://github.com/ufal/evalatin2024-latinpipe</a>.<br clear="both"> |
|
|
| --- |
|
|
| ## 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: |
| ```sh |
| (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](http://lindat.mff.cuni.cz/services/udpipe/) |
| and can be used either in a web form or through a REST service. |
|
|
| See the [latinpipe-evalatin24-240520 directory](latinpipe-evalatin24-240520/) 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 |
| ```sh |
| 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: |
| ```sh |
| 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" |
| } |
| ``` |
|
|
