@inproceedings {guillaume:halshs-03647315, title = {Fine-tuning pre-trained models for Automatic Speech Recognition: experiments on a fieldwork corpus of Japhug (Trans-Himalayan family)}, year = {2022}, address = {Dublin, Ireland}, keywords = {automatic speech recognition}, url = {https://shs.hal.science/halshs-03647315}, author = {Guillaume, S{\'e}verine and Wisniewski, Guillaume and Macaire, C{\'e}cile and Jacques, Guillaume and Michaud, Alexis and Galliot, Benjamin and Coavoux, Maximin and Rossato, Solange and Nguyễn, Minh-Ch{\^a}u and Fily, Maxime} } @inproceedings {guillaume:halshs-03647315, title = {Fine-tuning pre-trained models for Automatic Speech Recognition: experiments on a fieldwork corpus of Japhug (Trans-Himalayan family)}, year = {2022}, month = {May}, address = {Dublin, Ireland}, keywords = {automatic speech recognition}, url = {https://halshs.archives-ouvertes.fr/halshs-03647315}, author = {Guillaume, S{\'e}verine and Wisniewski, Guillaume and Macaire, C{\'e}cile and Jacques, Guillaume and Michaud, Alexis and Galliot, Benjamin and Coavoux, Maximin and Rossato, Solange and Nguyễn, Minh-Ch{\^a}u and Fily, Maxime} } @inproceedings {guillaume:halshs-03625580, title = {Les mod{\`e}les pr{\'e}-entra{\^\i}n{\'e}s {\`a} l{\textquoteright}{\'e}preuve des langues rares : exp{\'e}riences de reconnaissance de mots sur la langue japhug (sino-tib{\'e}tain)}, year = {2022}, month = {Jun}, address = {Noirmoutier, France}, keywords = {Apprentissage machine, automatic speech recognition, Computational Language Documentation, Deep learning, Documentation computationnelle des langues, Japhug, linguistic fieldwork, Linguistique de terrain, Open Science, Reconnaissance automatique de la parole, Science ouverte}, url = {https://halshs.archives-ouvertes.fr/halshs-03625580}, author = {Guillaume, S{\'e}verine and Wisniewski, Guillaume and Macaire, C{\'e}cile and Jacques, Guillaume and Michaud, Alexis and Galliot, Benjamin and Coavoux, Maximin and Rossato, Solange and Nguyễn, Minh-Ch{\^a}u and Fily, Maxime} } @inproceedings {guillaume:halshs-03625580, title = {Les mod{\`e}les pr{\'e}-entra{\^\i}n{\'e}s {\`a} l{\textquoteright}{\'e}preuve des langues rares : exp{\'e}riences de reconnaissance de mots sur la langue japhug (sino-tib{\'e}tain)}, year = {2022}, address = {Noirmoutier, France}, keywords = {Apprentissage machine, automatic speech recognition, Computational Language Documentation, Deep learning, Documentation computationnelle des langues, Japhug, linguistic fieldwork, Linguistique de terrain, Open Science, Reconnaissance automatique de la parole, Science ouverte}, url = {https://shs.hal.science/halshs-03625580}, author = {Guillaume, S{\'e}verine and Wisniewski, Guillaume and Macaire, C{\'e}cile and Jacques, Guillaume and Michaud, Alexis and Galliot, Benjamin and Coavoux, Maximin and Rossato, Solange and Nguyễn, Minh-Ch{\^a}u and Fily, Maxime} } @inproceedings {leFlauBERTModelesLangue2020, title = {FlauBERT : des mod{\`e}les de langue contextualis{\'e}s pr{\'e}-entra{\^\i}n{\'e}s pour le fran{\c c}ais (FlauBERT : Unsupervised Language Model Pre-training for French)}, year = {2020}, month = {jun}, pages = {268{\textendash}278}, publisher = {ATALA et AFCP}, address = {Nancy, France}, abstract = {Les mod{\`e}les de langue pr{\'e}-entra{\^\i}n{\'e}s sont d{\'e}sormais indispensables pour obtenir des r{\'e}sultats {\`a} l{\textquoteright}{\'e}tat-de-l{\textquoteright}art dans de nombreuses t{\^a}ches du TALN. Tirant avantage de l{\textquoteright}{\'e}norme quantit{\'e} de textes bruts disponibles, ils permettent d{\textquoteright}extraire des repr{\'e}sentations continues des mots, contextualis{\'e}es au niveau de la phrase. L{\textquoteright}efficacit{\'e} de ces repr{\'e}sentations pour r{\'e}soudre plusieurs t{\^a}ches de TALN a {\'e}t{\'e} d{\'e}montr{\'e}e r{\'e}cemment pour l{\textquoteright}anglais. Dans cet article, nous pr{\'e}sentons et partageons FlauBERT, un ensemble de mod{\`e}les appris sur un corpus fran{\c c}ais h{\'e}t{\'e}rog{\`e}ne et de taille importante. Des mod{\`e}les de complexit{\'e} diff{\'e}rente sont entra{\^\i}n{\'e}s {\`a} l{\textquoteright}aide du nouveau supercalculateur Jean Zay du CNRS. Nous {\'e}valuons nos mod{\`e}les de langue sur diverses t{\^a}ches en fran{\c c}ais (classification de textes, paraphrase, inf{\'e}rence en langage naturel, analyse syntaxique, d{\'e}sambigu{\"\i}sation automatique) et montrons qu{\textquoteright}ils surpassent souvent les autres approches sur le r{\'e}f{\'e}rentiel d{\textquoteright}{\'e}valuation FLUE {\'e}galement pr{\'e}sent{\'e} ici.}, author = {Le, Hang and Vial, Lo{\"\i}c and Frej, Jibril and Segonne, Vincent and Coavoux, Maximin and Lecouteux, Benjamin and Allauzen, Alexandre and Beno{\^\i}t Crabb{\'e} and Besacier, Laurent and Schwab, Didier} } @inproceedings {leFlauBERTUnsupervisedLanguage2020, title = {FlauBERT: Unsupervised Language Model Pre-training for French}, year = {2020}, month = {may}, pages = {2479{\textendash}2490}, publisher = {European Language Resources Association}, address = {Marseille, France}, abstract = {

Language models have become a key step to achieve state-of-the art results in many different Natural Language Processing (NLP) tasks. Leveraging the huge amount of unlabeled texts nowadays available, they provide an efficient way to pre-train continuous word representations that can be fine-tuned for a downstream task, along with their contextualization at the sentence level. This has been widely demonstrated for English using contextualized representations (Dai and Le, 2015; Peters et al., 2018; Howard and Ruder, 2018; Radford et al., 2018; Devlin et al., 2019; Yang et al., 2019b). In this paper, we introduce and share FlauBERT, a model learned on a very large and heterogeneous French corpus. Models of different sizes are trained using the new CNRS (French National Centre for Scientific Research) Jean Zay supercomputer. We apply our French language models to diverse NLP tasks (text classification, paraphrasing, natural language inference, parsing, word sense disambiguation) and show that most of the time they outperform other pre-training approaches. Different versions of FlauBERT as well as a unified evaluation protocol for the downstream tasks, called FLUE (French Language Understanding Evaluation), are shared to the research community for further reproducible experiments in French NLP.

}, isbn = {979-10-95546-34-4}, author = {Le, Hang and Vial, Lo{\"\i}c and Frej, Jibril and Segonne, Vincent and Coavoux, Maximin and Lecouteux, Benjamin and Allauzen, Alexandre and Beno{\^\i}t Crabb{\'e} and Besacier, Laurent and Schwab, Didier} } @article {coavouxUnlexicalizedTransitionbasedDiscontinuous2019a, title = {Unlexicalized Transition-based Discontinuous Constituency Parsing}, journal = {Transactions of the Association for Computational Linguistics}, volume = {7}, year = {2019}, pages = {73{\textendash}89}, abstract = {

Lexicalized parsing models are based on the assumptions that (i) constituents are organized around a lexical head and (ii) bilexical statistics are crucial to solve ambiguities. In this paper, we introduce an unlexicalized transition-based parser for discontinuous constituency structures, based on a structure-label transition system and a bi-LSTM scoring system. We compare it with lexicalized parsing models in order to address the question of lexicalization in the context of discontinuous constituency parsing. Our experiments show that unlexicalized models systematically achieve higher results than lexicalized models, and provide additional empirical evidence that lexicalization is not necessary to achieve strong parsing results. Our best unlexicalized model sets a new state of the art on English and German discontinuous constituency treebanks. We further provide a per-phenomenon analysis of its errors on discontinuous constituents.

}, author = {Coavoux, Maximin and Beno{\^\i}t Crabb{\'e} and Cohen, Shay B.} } @inproceedings {5629, title = {Cross-lingual RST Discourse Parsing}, year = {2017}, month = {April}, pages = {292{\textendash}304}, publisher = {Association for Computational Linguistics}, address = {Valencia, Spain}, abstract = {

Discourse parsing is an integral part of understanding information flow and argumentative structure in documents. Most previous research has focused on inducing and evaluating models from the English RST Discourse Treebank. However, discourse treebanks for other languages exist, including Spanish, German, Basque, Dutch and Brazilian Portuguese. The treebanks share the same underlying linguistic theory, but differ slightly in the way documents are annotated. In this paper, we present (a) a new discourse parser which is simpler, yet competitive (significantly better on 2/3 metrics) to state of the art for English, (b) a harmonization of discourse treebanks across languages, enabling us to present (c) what to the best of our knowledge are the first experiments on cross-lingual discourse parsing.

}, url = {http://www.aclweb.org/anthology/E17-1028}, author = {Braud, Chlo{\'e} and Coavoux, Maximin and S{\o}gaard, Anders} } @inproceedings {5627, title = {Incremental Discontinuous Phrase Structure Parsing with the GAP Transition}, year = {2017}, month = {April}, pages = {1259{\textendash}1270}, publisher = {Association for Computational Linguistics}, address = {Valencia, Spain}, abstract = {

This article introduces a novel transition system for discontinuous lexicalized constituent parsing called SR-GAP. It is an extension of the shift-reduce algorithm with an additional gap transition. Evaluation on two German treebanks shows that SR-GAP outperforms the previous best transition-based discontinuous parser (Maier, 2015) by a large margin (it is notably twice as accurate on the prediction of discontinuous constituents), and is competitive with the state of the art (Fern\ández-Gonz\ález and Martins, 2015). As a side contribution, we adapt span features (Hall et al., 2014) to discontinuous parsing.

}, url = {http://www.aclweb.org/anthology/E17-1118}, author = {Coavoux, Maximin and Beno{\^\i}t Crabb{\'e}} } @inproceedings {5628, title = {Multilingual Lexicalized Constituency Parsing with Word-Level Auxiliary Tasks}, year = {2017}, month = {April}, pages = {331{\textendash}336}, publisher = {Association for Computational Linguistics}, address = {Valencia, Spain}, abstract = {

We introduce a constituency parser based on a bi-LSTM encoder adapted from re- cent work (Cross and Huang, 2016b; Kiperwasser and Goldberg, 2016), which can incorporate a lower level character bi- LSTM (Ballesteros et al., 2015; Plank et al., 2016). We model two important in- terfaces of constituency parsing with aux- iliary tasks supervised at the word level: (i) part-of-speech (POS) and morpholog- ical tagging, (ii) functional label predic- tion. On the SPMRL dataset, our parser obtains above state-of-the-art results on constituency parsing without requiring ei- ther predicted POS or morphological tags, and outputs labelled dependency trees.

}, url = {http://www.aclweb.org/anthology/E17-2053}, author = {Coavoux, Maximin and Beno{\^\i}t Crabb{\'e}} } @inproceedings {5630, title = {Neural Greedy Constituent Parsing with Dynamic Oracles}, year = {2016}, month = {August}, pages = {172{\textendash}182}, publisher = {Association for Computational Linguistics}, address = {Berlin, Germany}, url = {http://www.aclweb.org/anthology/P16-1017}, author = {Coavoux, Maximin and Beno{\^\i}t Crabb{\'e}} } @inproceedings {coavoux:hal-01353734, title = {{Neural Greedy Constituent Parsing with Dynamic Oracles}}, year = {2016}, address = {Berlin, Germany}, keywords = {NLP, parsing}, url = {https://hal.inria.fr/hal-01353734}, author = {Coavoux, Maximin and Beno{\^\i}t Crabb{\'e}} } @article {5632, title = {Pr{\'e}diction structur{\'e}e pour l{\textquoteright}analyse syntaxique en constituants par transitions : mod{\`e}les denses et mod{\`e}les creux}, journal = {Traitement Automatique des Langues}, volume = {57}, year = {2016}, url = {https://hal.inria.fr/hal-01365252}, author = {Coavoux, Maximin and Beno{\^\i}t Crabb{\'e}} } @inproceedings {coavoux:hal-01174613, title = {{Comparaison d{\textquoteright}architectures neuronales pour l{\textquoteright}analyse syntaxique en constituants}}, year = {2015}, address = {Caen, France}, keywords = {Analyse syntaxique en constituants lexicalis{\'e}e, plongements lexicaux, r{\'e}seaux de neurones}, url = {https://hal.inria.fr/hal-01174613}, author = {Coavoux, Maximin and Beno{\^\i}t Crabb{\'e}} } @inproceedings {5631, title = {Comparaison d{\textquoteright}architectures neuronales pour l{\textquoteright}analyse syntaxique en constituants}, year = {2015}, month = {June}, pages = {293{\textendash}304}, publisher = {Association pour le Traitement Automatique des Langues}, address = {Caen, France}, url = {http://www.atala.org/taln_archives/TALN/TALN-2015/taln-2015-long-025}, author = {Coavoux, Maximin and Beno{\^\i}t Crabb{\'e}} }