Domain Adaptation for Question Answering via Question Classification

Zhenrui Yue, Huimin Zeng, Ziyi Kou, Lanyu Shang, Dong Wang


Abstract
Question answering (QA) has demonstrated impressive progress in answering questions from customized domains. Nevertheless, domain adaptation remains one of the most elusive challenges for QA systems, especially when QA systems are trained in a source domain but deployed in a different target domain. In this work, we investigate the potential benefits of question classification for QA domain adaptation. We propose a novel framework: Question Classification for Question Answering (QC4QA). Specifically, a question classifier is adopted to assign question classes to both the source and target data. Then, we perform joint training in a self-supervised fashion via pseudo-labeling. For optimization, inter-domain discrepancy between the source and target domain is reduced via maximum mean discrepancy (MMD) distance. We additionally minimize intra-class discrepancy among QA samples of the same question class for fine-grained adaptation performance. To the best of our knowledge, this is the first work in QA domain adaptation to leverage question classification with self-supervised adaptation. We demonstrate the effectiveness of the proposed QC4QA with consistent improvements against the state-of-the-art baselines on multiple datasets.
Anthology ID:
2022.coling-1.153
Volume:
Proceedings of the 29th International Conference on Computational Linguistics
Month:
October
Year:
2022
Address:
Gyeongju, Republic of Korea
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
1776–1790
Language:
URL:
https://aclanthology.org/2022.coling-1.153
DOI:
Bibkey:
Cite (ACL):
Zhenrui Yue, Huimin Zeng, Ziyi Kou, Lanyu Shang, and Dong Wang. 2022. Domain Adaptation for Question Answering via Question Classification. In Proceedings of the 29th International Conference on Computational Linguistics, pages 1776–1790, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
Cite (Informal):
Domain Adaptation for Question Answering via Question Classification (Yue et al., COLING 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.coling-1.153.pdf
Code
 yueeeeeeee/self-supervised-qa
Data
HotpotQANewsQASQuADSearchQA