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首页> 外文会议>International joint conference on natural language processing >Knowledge Aware Conversation Generation with Explainable Reasoning over Augmented Graphs
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Knowledge Aware Conversation Generation with Explainable Reasoning over Augmented Graphs

机译：知识意识的会话生成，可解释在增强的图形上

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摘要

Two types of knowledge, triples from knowledge graphs and texts from documents, have been studied for knowledge aware open-domain conversation generation, in which graph paths can narrow down vertex candidates for knowledge selection decision, and texts can provide rich information for response generation. Fusion of a knowledge graph and texts might yield mutually reinforcing advantages, but there is less study on that. To address this challenge, we propose a knowledge aware chatting machine with three components, an augmented knowledge graph with both triples and texts, knowledge selector, and knowledge aware response generator. For knowledge selection on the graph, we formulate it as a problem of multi-hop graph reasoning to effectively capture conversation flow, which is more explainable and flexible in comparison with previous work. To fully leverage long text information that differentiates our graph from others, we improve a state of the art reasoning algorithm with machine reading comprehension technology. We demonstrate the effectiveness of our system on two datasets in comparison with state-of-the-art models~1.
机译：已经研究了两种类型的知识，从知识图和文档中的文本进行了三倍，已经研究了知识意识的开放域对话生成，其中图表路径可以缩小到知识选择决策的顶点候选，并且文本可以为响应生成提供丰富的信息。知识图和文本的融合可能会产生相互加强的优势，但对此有较少的研究。为解决这一挑战，我们提出了一个知识意识的聊天机，其中包含三个组件，一个增强知识图，包括三元组和文本，知识选择器和知识意识响应发生器。对于图表中的知识选择，我们将其作为多跳图推理的问题，以有效地捕获对话流程，与之前的工作相比，更可说明和灵活。为了充分利用从其他人区分我们的图表的长文本信息，我们提高了机器阅读理解技术的艺术推理算法的状态。与最先进的模型相比，我们展示了我们系统对两个数据集的有效性〜1。

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