Fact-based Dialogue Generation with Convergent and Divergent Decoding

• URL: http://arxiv.org/abs/2005.03174v2
• Date: Fri, 8 May 2020 00:43:36 GMT
• Title: Fact-based Dialogue Generation with Convergent and Divergent Decoding
• Authors: Ryota Tanaka, Akinobu Lee
• Abstract summary: This paper proposes an end-to-end fact-based dialogue system augmented with the ability of convergent and divergent thinking. Our model incorporates a novel convergent and divergent decoding that can generate informative and diverse responses.
• Score: 2.28438857884398
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Fact-based dialogue generation is a task of generating a human-like response based on both dialogue context and factual texts. Various methods were proposed to focus on generating informative words that contain facts effectively. However, previous works implicitly assume a topic to be kept on a dialogue and usually converse passively, therefore the systems have a difficulty to generate diverse responses that provide meaningful information proactively. This paper proposes an end-to-end fact-based dialogue system augmented with the ability of convergent and divergent thinking over both context and facts, which can converse about the current topic or introduce a new topic. Specifically, our model incorporates a novel convergent and divergent decoding that can generate informative and diverse responses considering not only given inputs (context and facts) but also inputs-related topics. Both automatic and human evaluation results on DSTC7 dataset show that our model significantly outperforms state-of-the-art baselines, indicating that our model can generate more appropriate, informative, and diverse responses.

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