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

Explicit Alignment and Many-to-many Entailment Based Reasoning for Conversational Machine Reading