FQuAD: French Question Answering Dataset

02/14/2020
by   Martin d'Hoffschmidt, et al.
0

Recent advances in the field of language modeling have improved state-of-the-art results on many Natural Language Processing tasks. Among them, the Machine Reading Comprehension task has made significant progress. However, most of the results are reported in English since labeled resources available in other languages, such as French, remain scarce. In the present work, we introduce the French Question Answering Dataset (FQuAD). FQuAD is French Native Reading Comprehension dataset that consists of 25,000+ questions on a set of Wikipedia articles. A baseline model is trained which achieves an F1 score of 88.0 freely available at https://fquad.illuin.tech.

READ FULL TEXT

page 1

page 2

page 3

page 4

09/27/2021

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

Question Answering, including Reading Comprehension, is one of the NLP r...
06/16/2016

SQuAD: 100,000+ Questions for Machine Comprehension of Text

We present the Stanford Question Answering Dataset (SQuAD), a new readin...
07/15/2021

Automatic Task Requirements Writing Evaluation via Machine Reading Comprehension

Task requirements (TRs) writing is an important question type in Key Eng...
11/02/2021

UQuAD1.0: Development of an Urdu Question Answering Training Data for Machine Reading Comprehension

In recent years, low-resource Machine Reading Comprehension (MRC) has ma...
07/27/2021

QA Dataset Explosion: A Taxonomy of NLP Resources for Question Answering and Reading Comprehension

Alongside huge volumes of research on deep learning models in NLP in the...
06/24/2019

EQuANt (Enhanced Question Answer Network)

Machine Reading Comprehension (MRC) is an important topic in the domain ...
09/09/2019

Question Generation by Transformers

A machine learning model was developed to automatically generate questio...