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Found 12 papers, 1 papers with code
DrBenchmark: A Large Language Understanding Evaluation Benchmark for French Biomedical Domain
This limitation hampers the evaluation of the latest French biomedical models, as they are either assessed on a minimal number of tasks with non-standardized protocols or evaluated using general downstream tasks.
Building a Corpus for Biomedical Relation Extraction of Species Mentions
We present a manually annotated corpus, Species-Species Interaction, for extracting meaningful binary relations between species, in biomedical texts, at sentence level, with a focus on the gut microbiota.
A Multimodal Educational Corpus of Oral Courses: Annotation, Analysis and Case Study
The use cases concern scientific fields from both speech and text processing, with language model adaptation, thematic segmentation and transcription to slide alignment.
Automatic segmentation of texts into units of meaning for reading assistance
The emergence of the digital book is a major step forward in providing access to reading, and therefore often to the common culture and the labour market.
Cultural Vocal Bursts Intensity Prediction
Transfer Learning
Apport de l'adaptation automatique des mod\`eles de langage pour la reconnaissance de la parole: \'evaluation qualitative extrins\`eque dans un contexte de traitement de cours magistraux (Contribution of automatic adaptation of language models for speech recognition : extrinsic qualitative evaluation in a context of educational courses)
Les transcriptions automatiques de ces syst{\`e}mes sont de plus en plus exploitables et utilis{\'e}es dans des syst{\`e}mes complexes de traitement automatique du langage naturel, par exemple pour la traduction automatique, l{'}indexation, la recherche documentaire... Des {\'e}tudes r{\'e}centes ont propos{\'e} des m{\'e}triques permettant de comparer la qualit{\'e} des transcriptions automatiques de diff{\'e}rents syst{\`e}mes en fonction de la t{\^a}che vis{\'e}e. Dans cette {\'e}tude nous souhaitons mesurer, qualitativement, l{'}apport de l{'}adaptation automatique des mod{\`e}les de langage au domaine vis{\'e} par un cours magistral.
Transfer Learning for a Letter-Ngrams to Word Decoder in the Context of Historical Handwriting Recognition with Scarce Resources
We obtain a 97. 42{\%} Character Recognition Rate and a 86. 57{\%} Word Recognition Rate on our Italian Comedy data, despite a lexical coverage of 67{\%} between the Italian Comedy data and the training data.
D\'ecodeur neuronal pour la transcription de documents manuscrits anciens (Neural decoder for the transcription of historical handwritten documents)
Nous obtenons 97, 27{\%} de caract{\`e}res bien reconnus sur les donn{\'e}es de la Com{\'e}die-Italienne, ainsi que 86, 57{\%} de mots correctement g{\'e}n{\'e}r{\'e}s malgr{\'e} une couverture de 67, 58{\%} uniquement entre la Com{\'e}die-Italienne et l{'}ensemble d{'}apprentissage.
Segmentation automatique d'un texte en rh\`eses (Automatic segmentation of a text into rhesis)
La segmentation d{'}un texte en rh{\`e}ses, unit{\'e}s-membres signifiantes de la phrase, permet de fournir des adaptations de celui-ci pour faciliter la lecture aux personnes dyslexiques.
