Effectively Incorporating Knowledge in Open-Domain Dialogue Generation

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成果类型：

会议论文

作者：

Zhou W.;He T.;Zhang M.;Fan R.

通讯作者：

He, T.

作者机构：

Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning, Central China Normal University, Wuhan, Hubei, 430079, China

School of Computer, Central China Normal University, Wuhan, Hubei, 430079, China

National Engineering Research Center for E-Learning, Central China Normal University, Wuhan, Hubei, 430079, China

National Language Resources Monitor and Research Center for Network Media, Central China Normal University, Wuhan, Hubei, 430079, China

[Zhang M.] Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning, Central China Normal University, Wuhan, Hubei, 430079, China, National Engineering Research Center for E-Learning, Central China Normal University, Wuhan, Hubei, 430079, China, National Language Resources Monitor and Research Center for Network Media, Central China Normal University, Wuhan, Hubei, 430079, China

通讯机构：

[He, T.] H

Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning, China

语种：

英文

关键词：

Deep learning;Dialogue generation;External knowledge

期刊：

Communications in Computer and Information Science

ISSN：

1865-0929

年：

2021

卷：

1356 CCIS

页码：

237-249

会议名称：

5th China Conference on Knowledge Graph, and Semantic Computing, CCKS 2020

会议时间：

12 November 2020 through 15 November 2020

主编：

Chen H.Liu K.Sun Y.Wang S.Hou L.

出版者：

Springer Science and Business Media Deutschland GmbH

ISBN：

9789811619632

DOI：

10.1007/978-981-16-1964-9_19

基金类别：

Acknowledgments. This research is supported by the National Natural Science Foundation of China (61532008, 61932008, 61572223), the Key Research and Development Program of Hubei Province (2020BAB017), Wuhan Science and Technology Program (2019010701011392), Scientific Research Center Program of National Language Commission (ZDI135-135) and the National Key Research and Development Program of China (2017YFC0909502).

机构署名：

本校为第一机构

院系归属：

计算机学院

国家数字化学习工程技术研究中心

摘要：

Dialogue generation is one of the most important parts in the dialogue system. Generating useful and informative responses in conversation has become a research hotspot. Previous work has proved that incorporating external knowledge is conducive to generating meaningful responses. But how to make full use of the existing information to select the most appropriate knowledge is a current research difficulty. In this paper, we propose a dialogue generation model with a lightweight knowledge routing module to sample knowledge needed for the convers...

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Effectively Incorporating Knowledge in Open-Domain Dialogue Generation

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