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• W3089448099 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." @default.
• W3089448099 created "2020-10-08" @default.
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• W3089448099 date "2020-10-05" @default.
• W3089448099 modified "2023-09-27" @default.
• W3089448099 title "Discern: Discourse-Aware Entailment Reasoning Network for Conversational Machine Reading" @default.
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• W3089448099 doi "https://doi.org/10.48550/arxiv.2010.01838" @default.
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