Discern: Discourse-Aware Entailment Reasoning Network for Conversational Machine Reading

• URL: http://arxiv.org/abs/2010.01838v3
• Date: Fri, 16 Oct 2020 10:06:46 GMT
• Title: Discern: Discourse-Aware Entailment Reasoning Network for Conversational Machine Reading
• Authors: Yifan Gao, Chien-Sheng Wu, Jingjing Li, Shafiq Joty, Steven C.H. Hoi, Caiming Xiong, Irwin King, Michael R. Lyu
• Abstract summary: Discern is a discourse-aware entailment reasoning network to strengthen the connection and enhance the understanding for both document and dialog. Our experiments 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.
• Score: 157.14821839576678
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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