论文标题
主题感知的响应生成在以任务为导向的对话中，与非结构化知识访问
Topic-Aware Response Generation in Task-Oriented Dialogue with Unstructured Knowledge Access
论文作者
论文摘要
为了减轻结构化数据库有限的覆盖范围有限的问题，最近以任务为导向的对话系统结合了外部非结构化知识，以指导系统响应的产生。但是，这些通常使用单词或句子级别的相似性来检测相关的知识上下文，这只会部分捕获局部级别的相关性。在本文中，我们研究了如何更好地将主题信息整合到知识扎根的面向任务的对话中，并提出``主题感知响应生成''（TARG），这是一种端到端响应生成模型。塔格（Targ）结合了多种主题意识的注意机制，以推导与对话话语和外部知识源的重要性加权方案，以更好地理解对话历史。实验结果表明，Targ在知识选择和响应产生中实现了最新的表现，在EM，F1和BLEU-4中，在DOC2DIAL上分别优于3.2、3.6和4.2分的先前最新表现，并且与先前在DSTC9上的工作相当地执行。两者都是知识基础的面向任务的对话数据集。
To alleviate the problem of structured databases' limited coverage, recent task-oriented dialogue systems incorporate external unstructured knowledge to guide the generation of system responses. However, these usually use word or sentence level similarities to detect the relevant knowledge context, which only partially capture the topical level relevance. In this paper, we examine how to better integrate topical information in knowledge grounded task-oriented dialogue and propose ``Topic-Aware Response Generation'' (TARG), an end-to-end response generation model. TARG incorporates multiple topic-aware attention mechanisms to derive the importance weighting scheme over dialogue utterances and external knowledge sources towards a better understanding of the dialogue history. Experimental results indicate that TARG achieves state-of-the-art performance in knowledge selection and response generation, outperforming previous state-of-the-art by 3.2, 3.6, and 4.2 points in EM, F1 and BLEU-4 respectively on Doc2Dial, and performing comparably with previous work on DSTC9; both being knowledge-grounded task-oriented dialogue datasets.
