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
this https URL

本文提出了 Discern，透过将文件拆分成 EDU 单元，进行前置训练的话语分割模型，并以弱监督方式预测 EDU 是否为对话中用户反馈的被蕴含，提高文件和对话的理解，取得最高决策准确度 78.3% 及跟进问题生成 BLEU1 64.0 的成果。