@article {7399, title = {French word order : the role of animacy}, journal = {Glossa: a journal of general linguistics}, volume = {6}, year = {2021}, pages = {1-55}, chapter = {1}, abstract = {

A major goal of the quantitative study of syntax has been to identify factors that have predictive power on speaker choices in the face of word-order or valence alternations (e.g. Arnold et al. 2000; Bresnan et al. 2007; Bresnan \& Ford 2010; Bader \& H\äussler 2010). In this paper, we study the role of animacy on the order of constituents in French. Animacy has been shown to affect sentence production in other languages, either directly (Feleki \& Branigan 1999; Kempen \& Harbusch 2004; Tanaka et al. 2011) or indirectly through grammatical role assignment (McDonald et al. 1993). Corpus studies however, have failed to find such an effect in French (Thuilier 2012a; Thuilier et al. 2014). Using a sentence recall task, we examined whether animacy has an impact on linear ordering or on grammatical function assignment. While we do find evidence for a role of animacy in the choice between active and passive voice, we do not find a preference to place animate arguments first with ditransitive verbs nor with nominal coordinations. While these findings tend to support the indirect hypothesis (McDonald et al. 1993; Kempen \& Harbusch 2004), we also find what may look like an anti-animacy effect: inanimate direct objects tend to precede animate indirect objects. We propose that canonical mappings between syntactic function and semantic role play a role in putting (inanimate theme) direct objects before (animate recipient) indirect objects, thus overriding the animacy first tendency in French.

}, doi = {https://doi.org/10.5334/gjgl.1155}, url = {https://www.glossa-journal.org/article/id/5435/}, author = {Juliette Thuilier and Grant, Margaret and Beno{\^\i}t Crabb{\'e} and Anne Abeill{\'e}} } @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 {barque:hal-02511929, title = {FrSemCor: Annotating a French corpus with supersenses}, year = {2020}, month = {May}, address = {Marseille, France}, keywords = {corpus, semantic annotation, supersenses}, url = {https://hal.archives-ouvertes.fr/hal-02511929}, author = {Lucie Barque and Pauline Haas and Richard Huyghe and Delphine Tribout and Marie Candito and Beno{\^\i}t Crabb{\'e} and Segonne, V} } @inproceedings {barque:hal-02511929, title = {FrSemCor: Annotating a French corpus with supersenses}, year = {2020}, address = {Marseille, France}, keywords = {corpus, semantic annotation, supersenses}, url = {https://hal.archives-ouvertes.fr/hal-02511929}, author = {Lucie Barque and Richard Huyghe and Delphine Tribout and Marie Candito and Beno{\^\i}t Crabb{\'e} and Segonne, V} } @inproceedings {abeille:hal-00936493, title = {French Polar Questions : a corpus study}, year = {2013}, month = {Jul}, address = {Gen{\`e}ve, Switzerland}, url = {https://hal.inria.fr/hal-00936493}, author = {Anne Abeill{\'e} and Beno{\^\i}t Crabb{\'e} and Godard, Dani{\`e}le and Jean-Marie Marandin} } @inproceedings {abeille:hal-00936496, title = {French questioning declaratives : a corpus study}, year = {2012}, month = {Sep}, address = {Paris, France}, url = {https://hal.inria.fr/hal-00936496}, author = {Anne Abeill{\'e} and Beno{\^\i}t Crabb{\'e} and Godard, Dani{\`e}le and Jean-Marie Marandin}, editor = {Sarah Brown-Schmidt and Jonathan Ginzburg and Staffan Larsson} }