@inproceedings{gao-etal-2020-discern,
title = "Discern: Discourse-Aware Entailment Reasoning Network for Conversational Machine Reading",
author = "Gao, Yifan and
Wu, Chien-Sheng and
Li, Jingjing and
Joty, Shafiq and
Hoi, Steven C.H. and
Xiong, Caiming and
King, Irwin and
Lyu, Michael",
editor = "Webber, Bonnie and
Cohn, Trevor and
He, Yulan and
Liu, Yang",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.emnlp-main.191",
doi = "10.18653/v1/2020.emnlp-main.191",
pages = "2439--2449",
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 of 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 \url{https://github.com/Yifan-Gao/Discern}.",
}
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<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 of 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.</abstract>
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%0 Conference Proceedings
%T Discern: Discourse-Aware Entailment Reasoning Network for Conversational Machine Reading
%A Gao, Yifan
%A Wu, Chien-Sheng
%A Li, Jingjing
%A Joty, Shafiq
%A Hoi, Steven C.H.
%A Xiong, Caiming
%A King, Irwin
%A Lyu, Michael
%Y Webber, Bonnie
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F gao-etal-2020-discern
%X 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 of 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.
%R 10.18653/v1/2020.emnlp-main.191
%U https://aclanthology.org/2020.emnlp-main.191
%U https://doi.org/10.18653/v1/2020.emnlp-main.191
%P 2439-2449
Markdown (Informal)
[Discern: Discourse-Aware Entailment Reasoning Network for Conversational Machine Reading](https://aclanthology.org/2020.emnlp-main.191) (Gao et al., EMNLP 2020)
ACL