@inproceedings {simoulinUnifyingParsingTreeStructured2022, title = {Unifying Parsing and Tree-Structured Models for Generating Sentence Semantic Representations}, year = {2022}, pages = {267{\textendash}276}, publisher = {Association for Computational Linguistics}, address = {Hybrid: Seattle, Washington + Online}, abstract = {

We introduce a novel tree-based model that learns its composition function together with its structure. The architecture produces sentence embeddings by composing words according to an induced syntactic tree. The parsing and the composition functions are explicitly connected and, therefore, learned jointly. As a result, the sentence embedding is computed according to an interpretable linguistic pattern and may be used on any downstream task. We evaluate our encoder on downstream tasks, and we observe that it outperforms tree-based models relying on external parsers. In some configurations, it is even competitive with Bert base model. Our model is capable of supporting multiple parser architectures. We exploit this property to conduct an ablation study by comparing different parser initializations. We explore to which extent the trees produced by our model compare with linguistic structures and how this initialization impacts downstream performances. We empirically observe that downstream supervision troubles producing stable parses and preserving linguistically relevant structures.

}, author = {Simoulin, Antoine and Beno{\^\i}t Crabb{\'e}} } @inproceedings {simoulin:hal-03265900, title = {Un mod{\`e}le Transformer G{\'e}n{\'e}ratif Pr{\'e}-entrain{\'e} pour le ______ fran{\c c}ais}, year = {2021}, pages = {246-255}, publisher = {ATALA}, address = {Lille, France}, keywords = {fran{\c c}ais., G{\'e}n{\'e}ratif, GPT, Pr{\'e}-entra{\^\i}n{\'e}, Transformer}, url = {https://hal.archives-ouvertes.fr/hal-03265900}, author = {Simoulin, Antoine and Beno{\^\i}t Crabb{\'e}}, editor = {Denis, Pascal and Grabar, Natalia and Fraisse, Amel and Cardon, R{\'e}mi and Jacquemin, Bernard and Kergosien, Eric and Balvet, Antonio} } @inproceedings {simoulinModeleTransformerGeneratif2021, title = {Un mod{\`e}le Transformer G{\'e}n{\'e}ratif Pr{\'e}-entrain{\'e} pour le fran{\c c}ais Generative Pre-trained Transformer in French (We introduce a French adaptation from the well-known GPT model)}, year = {2021}, month = {jun}, pages = {246{\textendash}255}, publisher = {ATALA}, address = {Lille, France}, abstract = {

Nous proposons une adaptation en fran\çais du fameux mod\èle Generative Pre-trained Transformer (GPT). Ce dernier appartient \à la cat\égorie des architectures transformers qui ont significativement transform\é les m\éthodes de traitement automatique du langage. Ces architectures sont en particulier pr\é-entra\în\ées sur des t\âches auto-supervis\ées et sont ainsi sp\écifiques pour une langue donn\ée. Si certaines sont disponibles en fran\çais, la plupart se d\éclinent avant tout en anglais. GPT est particuli\èrement efficace pour les t\âches de g\én\ération de texte. Par ailleurs, il est possible de l\&$\#$39;appliquer \à de nombreux cas d\&$\#$39;usages. Ses propri\ét\és g\én\ératives singuli\ères permettent de l\&$\#$39;utiliser dans des conditions originales comme l\&$\#$39;apprentissage sans exemple qui ne suppose aucune mise \à jour des poids du mod\èle, ou modification de l\&$\#$39;architecture.

}, author = {Simoulin, Antoine and Beno{\^\i}t Crabb{\'e}} } @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 {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}} } @inproceedings {crabbe:hal-01105143, title = {{Un analyseur discriminant de la famille LR pour l{\textquoteright}analyse en constituants}}, year = {2014}, address = {Marseille, France}, url = {https://hal.inria.fr/hal-01105143}, author = {Beno{\^\i}t Crabb{\'e}} } @inproceedings {seddah:hal-00780899, title = {Ubiquitous Usage of a French Large Corpus: Processing the Est Republicain Corpus}, year = {2012}, month = {May}, publisher = {{ELRA}}, address = {Istambul, Turkey}, keywords = {corpus, out-of-domain, parsing}, url = {https://hal.inria.fr/hal-00780899}, author = {Seddah, Djam{\'e} and Marie Candito and Beno{\^\i}t Crabb{\'e} and Anguiano Enrique, Henestroza} }