SLICE: Supersense-based Lightweight Interpretable Contextual Embeddings

no code implementations • COLING 2020 • Cindy Aloui, Carlos Ramisch, Alexis Nasr, Lucie Barque

Contextualised embeddings such as BERT have become de facto state-of-the-art references in many NLP applications, thanks to their impressive performances.

CALOR-QUEST : generating a training corpus for Machine Reading Comprehension models from shallow semantic annotations

no code implementations • WS 2019 • Frederic Bechet, Cindy Aloui, Delphine Charlet, Geraldine Damnati, Johannes Heinecke, Alexis Nasr, Frederic Herledan

Machine reading comprehension is a task related to Question-Answering where questions are not generic in scope but are related to a particular document.

Machine Reading Comprehension Natural Questions +1

CALOR-QUEST : un corpus d'entra\^\inement et d'\'evaluation pour la compr\'ehension automatique de textes (Machine reading comprehension is a task related to Question-Answering where questions are not generic in scope but are related to a particular document)

no code implementations • JEPTALNRECITAL 2019 • Frederic Bechet, Cindy Aloui, Delphine Charlet, Geraldine Damnati, Johannes Heinecke, Alexis Nasr, Frederic Herledan

Le but de cette {\'e}tude est de permettre le d{\'e}veloppement de telles ressources pour d{'}autres langues {\`a} moindre co{\^u}t en proposant une m{\'e}thode g{\'e}n{\'e}rant de mani{\`e}re semi-automatique des questions {\`a} partir d{'}une analyse s{\'e}mantique d{'}un grand corpus.

Machine Reading Comprehension Question Answering