Conversational Machine Reading (CMR) requires answering a user's initial question through multi-turn dialogue interactions based on a given document. Although there exist many effective methods, they largely neglected the alignment between the document and the user-provided information, which significantly affects the intermediate decision-making and subsequent follow-up question generation. To address this issue, we propose a pipeline framework that (1) aligns the aforementioned two sides in an explicit way, (2)makes decisions using a lightweight many-to-many entailment reasoning module, and (3) directly generates follow-up questions based on the document and previously asked questions. Our proposed method achieves state-of-the-art in micro-accuracy and ranks the first place on the public leaderboard of the CMR benchmark dataset ShARC.

翻译：对话式机器阅读（CMR）要求在给定文档的基础上，通过多轮对话交互来回答用户的初始问题。尽管现有方法众多，但它们大多忽略了文档与用户提供信息之间的对齐，而这显著影响了中间决策过程及后续追问的生成。为解决此问题，我们提出一个流水线框架，该框架（1）以显式方式对齐上述两方面，（2）使用轻量级多对多蕴含推理模块进行决策，（3）基于文档和历史提问直接生成追问。我们的方法在微观准确率上达到最优水平，并在CMR基准数据集ShARC的公开排行榜上排名第一。