论文标题
准确的问题回答的双重检索和排名
Double Retrieval and Ranking for Accurate Question Answering
论文作者
论文摘要
最近的工作表明，基于变压器的答案选择模型中引入的答案验证步骤可以显着改善有关最新技术的状态。通过汇总顶级$ K $答案候选者的嵌入来支持目标答案的验证来执行此步骤。尽管该方法是直观的，声音仍然显示两个局限性：（i）仅根据问题而不是答案的相关性对支持候选人进行排名，以及（ii）其他答案候选人提供的支持是次优的，因为这些答案是独立于目标答案的。在本文中，我们通过提出（i）双重播放模型来解决这两个缺点，该模型为每个目标答案选择最佳支持； （ii）第二个神经检索阶段旨在将问答对编码为查询，该阶段找到了更具体的验证信息。 AS2的三个著名数据集上的结果表现出对最新技术状态的一致和显着改善。
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.
