@inproceedings{zhang-etal-2021-joint,
title = "Joint Models for Answer Verification in Question Answering Systems",
author = "Zhang, Zeyu and
Vu, Thuy and
Moschitti, Alessandro",
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",
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.",
}
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%0 Conference Proceedings
%T Joint Models for Answer Verification in Question Answering Systems
%A Zhang, Zeyu
%A Vu, Thuy
%A Moschitti, Alessandro
%S 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)
%D 2021
%8 aug
%I Association for Computational Linguistics
%C Online
%F zhang-etal-2021-joint
%X 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.
%R 10.18653/v1/2021.acl-long.252
%U https://aclanthology.org/2021.acl-long.252
%U https://doi.org/10.18653/v1/2021.acl-long.252
%P 3252-3262
Markdown (Informal)
[Joint Models for Answer Verification in Question Answering Systems](https://aclanthology.org/2021.acl-long.252) (Zhang et al., ACL 2021)
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.