Querying the knowledge base (KB) has long been a challenge in the end-to-end task-oriented dialogue system. Previous sequence-to-sequence (Seq2Seq) dialogue generation work treats the KB query as an attention over the entire KB, without the guarantee that the generated entities are consistent with each other. In this paper, we propose a novel framework which queries the KB in two steps to improve the consistency of generated entities. In the first step, inspired by the observation that a response can usually be supported by a single KB row, we introduce a KB retrieval component which explicitly returns the most relevant KB row given a dialogue history. The retrieval result is further used to filter the irrelevant entities in a Seq2Seq response generation model to improve the consistency among the output entities.In the second step, we further perform the attention mechanism to address the most correlated KB column. Two methods are proposed to make the training feasible without labeled retrieval data, which include distant supervision and Gumbel-Softmax technique. Experiments on two publicly available task oriented dialog datasets show the effectiveness of our model by outperforming the baseline systems and producing entity-consistent responses.

本文提出了一种新的框架，通过两个步骤查询知识库以提高生成的实体的一致性，第一步是使用KB检索组件检索最相关的KB行，以过滤Seq2Seq响应生成模型中的无关实体，第二步则进一步执行关注机制以解决相关的KB列。两种方法被提出来使训练可行，包括远程监控和Gumbel-Softmax技术。在两个公共数据集上进行的实验表明，我们的模型通过优于基准系统，在生成一致实体响应方面具有较好的效果。

具备实体一致性的端到端任务导向对话系统与知识库检索器