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As conventional answer selection (AS) methods generally match the questionwith each candidate answer independently, they suffer from the lack of matchinginformation between the question and the candidate. To address this problem, wepropose a novel reinforcement learning (RL) based multi-step ranking model,named MS-Ranker, which accumulates information from potentially correctcandidate answers as extra evidence for matching the question with a candidate.In specific, we explicitly consider the potential correctness of candidates andupdate the evidence with a gating mechanism. Moreover, as we use a listwiseranking reward, our model learns to pay more attention to the overallperformance. Experiments on two benchmarks, namely WikiQA and SemEval-2016 CQA,show that our model significantly outperforms existing methods that do not relyon external resources.

MS-Ranker: Accumulating Evidence from Potentially Correct Candidates for Answer Selection