Papers by Emmanuelle Esperança-Rodier
Online Versus Offline NMT Quality: An In-depth Analysis on English-German and German-English (2020.coling-main)
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Maha Elbayad, Michael Ustaszewski, Emmanuelle Esperança-Rodier, Francis Brunet-Manquat, Jakob Verbeek, Laurent Besacier
| Challenge: | Existing studies compare offline and online neural machine translation architectures . we examine the impact of online decoding constraints on the translation quality . |
| Approach: | They evaluate offline and online neural machine translation architectures using human evaluations on English-German and German-English language pairs. |
| Outcome: | The proposed models are particularly sensitive to latency constraints and are well-suited for offline translation tasks. |
A Multimodal French Corpus of Aligned Speech, Text, and Pictogram Sequences for Speech-to-Pictogram Machine Translation (2024.lrec-main)
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Cécile Macaire, Chloé Dion, Jordan Arrigo, Claire Lemaire, Emmanuelle Esperança-Rodier, Benjamin Lecouteux, Didier Schwab
| Challenge: | Existing algorithms for the automatic translation of spoken language into pictogram units are lacking for language impairments. |
| Approach: | They propose to use a French dataset that contains 230 hours of speech resources to create a rule-based pictogram grammar with a restricted vocabulary and a discussion of strategic decisions. |
| Outcome: | The proposed model is validated through multiple post-editing phases by expert annotators and is freely available under a non-commercial licence. |
DOLFIN - Document-Level Financial Test-Set for Machine Translation (2025.findings-naacl)
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| Challenge: | Existing document-level machine translation test-sets cover general domain but fall short on specialised domains, such as legal and financial. |
| Approach: | They propose to use a document-level machine translation test-set to replace perfectly aligned sentences by presenting data in units of sections rather than sentences. |
| Outcome: | The proposed dataset is built from specialised financial documents and it shows that it can discriminate between context-sensitive and context-agnostic models and shows the weaknesses when models fail to accurately translate financial texts. |
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. |