Alexandre Daniel Audibert


2024

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Jargon: A Suite of Language Models and Evaluation Tasks for French Specialized Domains
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
Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)

Pretrained Language Models (PLMs) are the de facto backbone of most state-of-the-art NLP systems. In this paper, we introduce a family of domain-specific pretrained PLMs for French, focusing on three important domains: transcribed speech, medicine, and law. We use a transformer architecture based on efficient methods (LinFormer) to maximise their utility, since these domains often involve processing long documents. We evaluate and compare our models to state-of-the-art models on a diverse set of tasks and datasets, some of which are introduced in this paper. We gather the datasets into a new French-language evaluation benchmark for these three domains. We also compare various training configurations: continued pretraining, pretraining from scratch, as well as single- and multi-domain pretraining. Extensive domain-specific experiments show that it is possible to attain competitive downstream performance even when pre-training with the approximative LinFormer attention mechanism. For full reproducibility, we release the models and pretraining data, as well as contributed datasets.