Papers by Vincent Segonne
FrSemCor: Annotating a French Corpus with Supersenses (2020.lrec-1)
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Lucie Barque, Pauline Haas, Richard Huyghe, Delphine Tribout, Marie Candito, Benoit Crabbé, Vincent Segonne
| Challenge: | a new project aims to provide a sense-annotated corpus of French for NLP and linguistics research . the project uses WordNet Unique Beginners as semantic tags to provide interoperability . |
| Approach: | They propose to use WordNet Unique Beginners as semantic tags to annotate French nouns . the project aims to provide a gold standard resource for linguistics and linguistic research . |
| Outcome: | The proposed resource is released online under a Creative Commons license. |
A Comparison of Language Modeling and Translation as Multilingual Pretraining Objectives (2024.emnlp-main)
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| Challenge: | Pretrained language models (PLMs) display impressive performances and have captured the attention of the NLP community. |
| Approach: | They propose to compare multilingual pretraining objectives in a controlled methodological environment with multilingual models. |
| Outcome: | The proposed model outperforms existing models in 6 languages and demonstrates that multilingual translation is an effective pretraining objective under the right conditions. |
FlauBERT: Unsupervised Language Model Pre-training for French (2020.lrec-1)
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Hang Le, Loïc Vial, Jibril Frej, Vincent Segonne, Maximin Coavoux, Benjamin Lecouteux, Alexandre Allauzen, Benoit Crabbé, Laurent Besacier, Didier Schwab
| Challenge: | Language models are a key step to achieve state-of-the-art results in many different Natural Language Processing (NLP) tasks. |
| Approach: | They propose to use a language model that is pre-trained on a large and heterogeneous French corpus to train continuous word representations. |
| Outcome: | The proposed model outperforms existing models on a large and heterogeneous French corpus. |
Limitations of Human Identification of Automatically Generated Text (2024.lrec-main)
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Nadège Alavoine, Maximin Coavoux, Emmanuelle Esperança-Rodier, Romane Gallienne, Carlos Gonzalez Gallardo, Jérôme Goulian, Jose G. Moreno, Aurélie Névéol, Didier Schwab, Vincent Segonne, Johanna Simoens
| Challenge: | Neural text generation tools such as ChatGPT are gaining popularity . human annotations are considered gold standard labels for multiple tasks . |
| Approach: | They propose a new corpus in French and English for recognising automatically generated texts . they propose 'incontext' setup which makes explicit the interaction between two parties . |
| Outcome: | The proposed model generates fluent text, which requires much closer reading than the current model. |
Jargon: A Suite of Language Models and Evaluation Tasks for French Specialized Domains (2024.lrec-main)
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Vincent Segonne, Aidan Mannion, Laura Cristina Alonzo Canul, Alexandre Daniel Audibert, Xingyu Liu, Cécile Macaire, Adrien Pupier, Yongxin Zhou, Mathilde Aguiar, Felix E. Herron, Magali Norré, Massih R Amini, Pierrette Bouillon, Iris Eshkol-Taravella, Emmanuelle Esperança-Rodier, Thomas François, Lorraine Goeuriot, Jérôme Goulian, Mathieu Lafourcade, Benjamin Lecouteux, François Portet, Fabien Ringeval, Vincent Vandeghinste, Maximin Coavoux, Marco Dinarelli, Didier Schwab
| Challenge: | Pretrained language models are the de facto backbone of most state-of-the-art NLP systems. |
| Approach: | They propose a family of domain-specific pretrained PLMs for French focusing on three important domains: transcribed speech, medicine, and law. |
| Outcome: | The proposed models perform better on transcribed speech, medicine, and law domains than state-of-the-art models on a diverse set of tasks and datasets. |
Can Machine Translation Bridge Multilingual Pretraining and Cross-lingual Transfer Learning? (2024.lrec-main)
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| Challenge: | Existing models that pretrain for cross-lingual tasks do not improve cross-linguistic learning. |
| Approach: | They propose to employ machine translation as a continued training objective to enhance language representation learning by bridging multilingual pretraining and cross-lingual applications. |
| Outcome: | The proposed model performance is compared with existing models and their latent representations. |