  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.
