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.
翻译:最近的工作表明,在以变换器为基础的答案选择模型中引入的回答核实步骤可以大大改善问题回答中的最新状态。这一步骤是通过将顶尖的$k回答候选人的嵌入合并以支持目标答案的核实来完成的。虽然这种方法直观和声音仍然显示出两个局限性:(一) 支持候选人的排名仅根据与问题的相关性,而不是根据答案,以及(二) 其他回答候选人提供的支持不尽人意,因为这些支持是独立于目标答案之外的检索。在本文件中,我们通过提出(一) 双排模型来解决这两个缺陷,该模型针对每个目标答案选择最佳支持;(二) 第二个神经检索阶段,旨在将问答作为查询编码,找到更具体的核实信息。关于AS2的三个众所周知的数据集的结果显示艺术状态的一致和显著改进。