@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} } @inproceedings {barqueFrSemCorAnnotatingFrench2020, title = {FrSemCor: Annotating a French Corpus with Supersenses}, year = {2020}, pages = {5912{\textendash}5918}, publisher = {European Language Resources Association}, address = {Marseille, France}, abstract = {

French, as many languages, lacks semantically annotated corpus data. Our aim is to provide the linguistic and NLP research communities with a gold standard sense-annotated corpus of French, using WordNet Unique Beginners as semantic tags, thus allowing for interoperability. In this paper, we report on the first phase of the project, which focused on the annotation of common nouns. The resulting dataset consists of more than 12,000 French noun occurrences which were annotated in double blind and adjudicated according to a carefully redefined set of supersenses. The resource is released online under a Creative Commons Licence.

}, isbn = {979-10-95546-34-4}, author = {Lucie Barque and Richard Huyghe and Delphine Tribout and Marie Candito and Beno{\^\i}t Crabb{\'e} and Segonne, Vincent} } @inproceedings {segonneUsingWiktionaryResource2019, title = {Using Wiktionary as a Resource for WSD : The Case of French Verbs}, year = {2019}, month = {may}, pages = {259{\textendash}270}, publisher = {Association for Computational Linguistics}, address = {Gothenburg, Sweden}, abstract = {

As opposed to word sense induction, word sense disambiguation (WSD) has the advantage of us-ing interpretable senses, but requires annotated data, which are quite rare for most languages except English (Miller et al. 1993; Fellbaum, 1998). In this paper, we investigate which strategy to adopt to achieve WSD for languages lacking data that was annotated specifically for the task, focusing on the particular case of verb disambiguation in French. We first study the usability of Eurosense (Bovi et al. 2017) , a multilingual corpus extracted from Europarl (Kohen, 2005) and automatically annotated with BabelNet (Navigli and Ponzetto, 2010) senses. Such a resource opened up the way to supervised and semi-supervised WSD for resourceless languages like French. While this perspective looked promising, our evaluation on French verbs was inconclusive and showed the annotated senses\&$\#$39; quality was not sufficient for supervised WSD on French verbs. Instead, we propose to use Wiktionary, a collaboratively edited, multilingual online dictionary, as a resource for WSD. Wiktionary provides both sense inventory and manually sense tagged examples which can be used to train supervised and semi-supervised WSD systems. Yet, because senses\&$\#$39; distribution differ in lexicographic examples found in Wiktionary with respect to natural text, we then focus on studying the impact on WSD of the training data size and senses\&$\#$39; distribution. Using state-of-the art semi-supervised systems, we report experiments of Wiktionary-based WSD for French verbs, evaluated on FrenchSemEval (FSE), a new dataset of French verbs manually annotated with wiktionary senses.

}, author = {Segonne, Vincent and Marie Candito and Beno{\^\i}t Crabb{\'e}} } @inproceedings {segonne:hal-02436417, title = {Using Wiktionary as a resource for WSD : the case of French verbs}, year = {2019}, pages = {259-270}, publisher = {Association for Computational Linguistics}, address = {Gothenburg, Sweden}, doi = {10.18653/v1/W19-0422}, url = {https://hal.archives-ouvertes.fr/hal-02436417}, author = {Segonne, Vincent and Marie Candito and Beno{\^\i}t Crabb{\'e}} }