  Conversational machine comprehension requires deep understanding of the
dialogue flow, and the prior work proposed FlowQA to implicitly model the
context representations in reasoning for better understanding. This paper
proposes to explicitly model the information gain through dialogue reasoning in
order to allow the model to focus on more informative cues. The proposed model
achieves state-of-the-art performance in a conversational QA dataset QuAC and
sequential instruction understanding dataset SCONE, which shows the
effectiveness of the proposed mechanism and demonstrates its capability of
generalization to different QA models and tasks.
