Joint Models for Answer Verification in Question Answering Systems [article]

Zeyu Zhang, Thuy Vu, Alessandro Moschitti
2021 arXiv   pre-print
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
more » ... er 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.
arXiv:2107.04217v1 fatcat:z3vvruhxfzcqnbqgpu7u6f4p2e