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.
BibTeX
@inproceedings{zhang-etal-2021-joint,
title = "Joint Models for Answer Verification in Question Answering Systems",
author = "Zhang, Zeyu and
Vu, Thuy and
Moschitti, Alessandro",
editor = "Zong, Chengqing and
Xia, Fei and
Li, Wenjie and
Navigli, Roberto",
booktitle = "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 = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.acl-long.252/",
doi = "10.18653/v1/2021.acl-long.252",
pages = "3252--3262"
}