Explicit Alignment and Many-to-many Entailment Based Reasoning for
  Conversational Machine Reading
Yangyang Luo Shiyu Tian Caixia Yuan Xiaojie Wang
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.