Papers by Jérôme Goulian
Growing Trees on Sounds: Assessing Strategies for End-to-End Dependency Parsing of Speech (2024.acl-short)
Copied to clipboard
| Challenge: | Direct dependency parsing of the speech signal is proposed as a way of incorporating prosodic information into the parser and bypassing the limitations of a pipeline approach. |
| Approach: | They propose to use graph-based parsing and sequence labeling based parses to integrate prosodic information into the parser and bypass limitations of pipeline approaches. |
| Outcome: | The proposed graph based approach outperforms a pipeline approach on a large treebank of spoken french, despite having 30% fewer parameters. |
Limitations of Human Identification of Automatically Generated Text (2024.lrec-main)
Copied to clipboard
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)
Copied to clipboard
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. |