Unsupervised multiple-choice question generation for out-of-domain Q&A fine-tuning
Guillaume Le Berre, Christophe Cerisara, Philippe Langlais, Guy Lapalme
Abstract
Pre-trained models have shown very good performances on a number of question answering benchmarks especially when fine-tuned on multiple question answering datasets at once. In this work, we propose an approach for generating a fine-tuning dataset thanks to a rule-based algorithm that generates questions and answers from unannotated sentences. We show that the state-of-the-art model UnifiedQA can greatly benefit from such a system on a multiple-choice benchmark about physics, biology and chemistry it has never been trained on. We further show that improved performances may be obtained by selecting the most challenging distractors (wrong answers), with a dedicated ranker based on a pretrained RoBERTa model.
BibTeX
@inproceedings{le-berre-etal-2022-unsupervised,
title = "Unsupervised multiple-choice question generation for out-of-domain {Q}{\&}{A} fine-tuning",
author = "Le Berre, Guillaume and
Cerisara, Christophe and
Langlais, Philippe and
Lapalme, Guy",
editor = "Muresan, Smaranda and
Nakov, Preslav and
Villavicencio, Aline",
booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
month = may,
year = "2022",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.acl-short.83/",
doi = "10.18653/v1/2022.acl-short.83",
pages = "732--738"
}