Joint Models for Answer Verification in Question Answering Systems

• URL: http://arxiv.org/abs/2107.04217v1
• Date: Fri, 9 Jul 2021 05:34:36 GMT
• Title: Joint Models for Answer Verification in Question Answering Systems
• Authors: Zeyu Zhang, Thuy Vu, and Alessandro Moschitti
• Abstract summary: We build a three-way multi-classifier, which decides if an answer supports, refutes, or is neutral with respect to another one. We tested our models on WikiQA, TREC-QA, and a real-world dataset.
• Score: 85.93456768689404
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 exploit 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 model 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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