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Building Dialogue Structure from Discourse Tree of a Question

机译:从问题的话语树构建对话结构

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摘要

In this section we propose a reasoning-based approach to a dialogue management for a customer support chat bot. To build a dialogue scenario, we analyze the discourse tree (DT) of an initial query of a customer support dialogue that is frequently complex and multi-sentence. We then enforce rhetorical agreement between DT of the initial query and that of the answers, requests and responses. The chat bot finds answers, which are not only relevant by topic but also suitable for a given step of a conversation and match the question by style, communication means, experience level and other domain-independent attributes. We evaluate a performance of proposed algorithm in car repair domain and observe a 5 to 10% improvement for single and three-step dialogues respectively, in comparison with baseline approaches to dialogue management.
机译:在本节中,我们为客户支持聊天机器人提出了一种基于推理的对话管理方法。为了建立对话场景,我们分析了一个客户支持对话的初始查询的语篇树(DT),该对话通常是复杂且多句子的。然后,我们在初始查询的DT与答案,请求和响应的DT之间强制执行修辞协议。聊天机器人会找到答案,这些答案不仅与主题相关,而且适合于对话的给定步骤,并通过样式,交流方式,经验水平和其他与域无关的属性来匹配问题。我们评估了所提出算法在汽车维修领域的性能,与对话管理的基线方法相比,单步和三步对话的观察结果分别提高了5%至10%。

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