论文标题
识别：对话机读取的话语意识到的核心推理网络
Discern: Discourse-Aware Entailment Reasoning Network for Conversational Machine Reading
论文作者
论文摘要
文档解释和对话了解是对话机读数的两个主要挑战。在这项工作中，我们提出了识别，这是一个意识到的需要推理网络，以加强联系并增强对文档和对话的理解。具体而言，我们使用预训练的话语细分模型将文档分为类似子句的基本话语单元（EDU），并以弱监督的方式训练我们的模型，以预测对话中用户反馈是否需要每个EDU。基于学识渊博的EDU和索引表示形式，我们要么回答用户最终的决定“是/否/无关紧要”的最初问题，要么生成后续问题以查询更多信息。我们在Sharc基准测试（盲目的，排出测试集）上进行的实验表明，Disesern在决策中实现了78.3％的宏观平均精度的最新结果，而在后续问题生成中，有64.0 BLEU1。代码和模型在https://github.com/yifan-gao/discern上发布。
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.
