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