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This paper studies joint models for selecting correct answer sentences amongthe top $k$ provided by answer sentence selection (AS2) modules, which are corecomponents of retrieval-based Question Answering (QA) systems. Our work showsthat a critical step to effectively exploit an answer set regards modeling theinterrelated information between pair of answers. For this purpose, we build athree-way multi-classifier, which decides if an answer supports, refutes, or isneutral with respect to another one. More specifically, our neural architectureintegrates a state-of-the-art AS2 model with the multi-classifier, and a jointlayer connecting all components. We tested our models on WikiQA, TREC-QA, and areal-world dataset. The results show that our models obtain the new state ofthe art in AS2.

Joint Models for Answer Verification in Question Answering Systems