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.
@article{arxiv.2107.04217,
title = {Joint Models for Answer Verification in Question Answering Systems},
author = {Zeyu Zhang and Thuy Vu and Alessandro Moschitti},
journal= {arXiv preprint arXiv:2107.04217},
year = {2021}
}