Joint Models for Answer Verification in Question Answering Systems

Zeyu Zhang, Thuy Vu, Alessandro Moschitti


Abstract
This paper studies joint models for selecting correct answer sentences among the top k provided by answer sentence selection (AS2) modules, which are core components of retrieval-based Question Answering (QA) systems. Our work shows that a critical step to effectively exploiting an answer set regards modeling the interrelated information between pair of answers. For this purpose, we build a three-way multi-classifier, which decides if an answer supports, refutes, or is neutral with respect to another one. More specifically, our neural architecture integrates a state-of-the-art AS2 module with the multi-classifier, and a joint layer connecting all components. We tested our models on WikiQA, TREC-QA, and a real-world dataset. The results show that our models obtain the new state of the art in AS2.
Anthology ID:
2021.acl-long.252
Volume:
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
Month:
August
Year:
2021
Address:
Online
Venues:
ACL | IJCNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
3252–3262
Language:
URL:
https://aclanthology.org/2021.acl-long.252
DOI:
10.18653/v1/2021.acl-long.252
Bibkey:
Cite (ACL):
Zeyu Zhang, Thuy Vu, and Alessandro Moschitti. 2021. Joint Models for Answer Verification in Question Answering Systems. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 3252–3262, Online. Association for Computational Linguistics.
Cite (Informal):
Joint Models for Answer Verification in Question Answering Systems (Zhang et al., ACL 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.acl-long.252.pdf
Video:
 https://aclanthology.org/2021.acl-long.252.mp4
Data
ASNQFEVERTrecQAWikiQA