  Recent work has shown that an answer verification step introduced in
Transformer-based answer selection models can significantly improve the state
of the art in Question Answering. This step is performed by aggregating the
embeddings of top $k$ answer candidates to support the verification of a target
answer. Although the approach is intuitive and sound still shows two
limitations: (i) the supporting candidates are ranked only according to the
relevancy with the question and not with the answer, and (ii) the support
provided by the other answer candidates is suboptimal as these are retrieved
independently of the target answer. In this paper, we address both drawbacks by
proposing (i) a double reranking model, which, for each target answer, selects
the best support; and (ii) a second neural retrieval stage designed to encode
question and answer pair as the query, which finds more specific verification
information. The results on three well-known datasets for AS2 show consistent
and significant improvement of the state of the art.
