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arXiv · 2007.01652

Generating Informative Dialogue Responses with Keywords-Guided Networks

Heng-Da Xu·Xian-Ling Mao·Zewen Chi·Jing-Jing Zhu·Fanshu Sun·Heyan Huang
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Abstract

Recently, open-domain dialogue systems have attracted growing attention. Most of them use the sequence-to-sequence (Seq2Seq) architecture to generate responses. However, traditional Seq2Seq-based open-domain dialogue models tend to generate generic and safe responses, which are less informative, unlike human responses. In this paper, we propose a simple but effective keywords-guided Sequence-to-Sequence model (KW-Seq2Seq) which uses keywords information as guidance to generate open-domain dialogue responses. Specifically, KW-Seq2Seq first uses a keywords decoder to predict some topic keywords, and then generates the final response under the guidance of them. Extensive experiments demonstrate that the KW-Seq2Seq model produces more informative, coherent and fluent responses, yielding substantive gain in both automatic and human evaluation metrics.

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Heng-Da Xu, Xian-Ling Mao, Zewen Chi, Jing-Jing Zhu, Fanshu Sun, Heyan Huang. 2020-07-03. Generating Informative Dialogue Responses with Keywords-Guided Networks. https://arxiv.org/abs/2007.01652

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