  Document interpretation and dialog understanding are the two major challenges
for conversational machine reading. In this work, we propose Discern, a
discourse-aware entailment reasoning network to strengthen the connection and
enhance the understanding for both document and dialog. Specifically, we split
the document into clause-like elementary discourse units (EDU) using a
pre-trained discourse segmentation model, and we train our model in a
weakly-supervised manner to predict whether each EDU is entailed by the user
feedback in a conversation. Based on the learned EDU and entailment
representations, we either reply to the user our final decision
"yes/no/irrelevant" of the initial question, or generate a follow-up question
to inquiry more information. Our experiments on the ShARC benchmark (blind,
held-out test set) show that Discern achieves state-of-the-art results of 78.3%
macro-averaged accuracy on decision making and 64.0 BLEU1 on follow-up question
generation. Code and models are released at
https://github.com/Yifan-Gao/Discern.

Computation and Language;Artificial Intelligence;Machine Learning