@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 = {<p>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.</p>
},
	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 = {<p>Nous proposons une adaptation en fran\&ccedil;ais du fameux mod\&egrave;le Generative Pre-trained Transformer (GPT). Ce dernier appartient \&agrave; la cat\&eacute;gorie des architectures transformers qui ont significativement transform\&eacute; les m\&eacute;thodes de traitement automatique du langage. Ces architectures sont en particulier pr\&eacute;-entra\&icirc;n\&eacute;es sur des t\&acirc;ches auto-supervis\&eacute;es et sont ainsi sp\&eacute;cifiques pour une langue donn\&eacute;e. Si certaines sont disponibles en fran\&ccedil;ais, la plupart se d\&eacute;clinent avant tout en anglais. GPT est particuli\&egrave;rement efficace pour les t\&acirc;ches de g\&eacute;n\&eacute;ration de texte. Par ailleurs, il est possible de l\&$\#$39;appliquer \&agrave; de nombreux cas d\&$\#$39;usages. Ses propri\&eacute;t\&eacute;s g\&eacute;n\&eacute;ratives singuli\&egrave;res permettent de l\&$\#$39;utiliser dans des conditions originales comme l\&$\#$39;apprentissage sans exemple qui ne suppose aucune mise \&agrave; jour des poids du mod\&egrave;le, ou modification de l\&$\#$39;architecture.</p>
},
	author = {Simoulin, Antoine and Beno{\^\i}t Crabb{\'e}}
}
