FQuAD2.0: French Question Answering and Learning When You Don’t Know

no code implementations • LREC 2022 • Quentin Heinrich, Gautier Viaud, Wacim Belblidia

This new dataset, comprising a total of almost 80, 000 questions, makes it possible to train French Question Answering models with the ability of distinguishing unanswerable questions from answerable ones.

Language Modelling Question Answering +1

FQuAD2.0: French Question Answering and knowing that you know nothing

no code implementations • 27 Sep 2021 • Quentin Heinrich, Gautier Viaud, Wacim Belblidia

This new dataset, comprising a total of almost 80, 000 questions, makes it possible to train French Question Answering models with the ability of distinguishing unanswerable questions from answerable ones.

Language Modelling Question Answering +1

Structural analysis of an all-purpose question answering model

no code implementations • 13 Apr 2021 • Vincent Micheli, Quentin Heinrich, François Fleuret, Wacim Belblidia

Attention is a key component of the now ubiquitous pre-trained language models.

Question Answering

FQuAD: French Question Answering Dataset

no code implementations • Findings of the Association for Computational Linguistics 2020 • Martin d'Hoffschmidt, Wacim Belblidia, Tom Brendlé, Quentin Heinrich, Maxime Vidal

FQuAD is a French Native Reading Comprehension dataset of questions and answers on a set of Wikipedia articles that consists of 25, 000+ samples for the 1. 0 version and 60, 000+ samples for the 1. 1 version.

Language Modelling Machine Reading Comprehension +1