MS-Ranker: Accumulating evidence from potentially correct candidates via reinforcement learning for answer selection

Yingxue Zhang, Fandong Meng, Peng Li, Ping Jian, Jie Zhou

Abstract: Answer selection (AS) aims to select correct answers for a question from an answer candidate set. Conventional AS methods generally address this task by independently matching the question and each candidate. However, since the matching information between the question and a single candidate is usually limited, it is not enough to use the question as the only evidence to estimate the correctness of each candidate. To address this problem, we propose a novel reinforcement learning (RL) based multi-step ranking model, named MS-Ranker, which accumulates candidate. In specific, we explicitly consider the potential correctness of candidates when accumulating information 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 three benchmarks, namely WikiQA, SemEval-2016 CQA and SelQA, show that our model significantly outperforms existing methods that do not rely on external resources.