Joint Learning of response ranking and next utterance suggestion in human-computer conversation system

Rui Yan, Dongyan Zhao, E. Weinan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

31 Scopus citations

Abstract

Conversation systems are of growing importance since they enable an easy interaction interface between humans and computers: using natural languages. To build a conversation system with adequate intelligence is challenging, and requires abundant resources including an acquisition of big data and interdisciplinary techniques, such as information retrieval and natural language processing. Along with the prosperity of Web 2.0, the massive data available greatly facilitate data-driven methods such as deep learning for humancomputer conversation systems. Owing to the diversity of Web resources, a retrieval-based conversation system will come up with at least some results from the immense repository for any user inputs. Given a human issued message, i.e., query, a traditional conversation system would provide a response after adequate training and learning of how to respond. In this paper, we propose a new task for conversation systems: joint learning of response ranking featured with next utterance suggestion. We assume that the new conversation mode is more proactive and keeps user engaging. We examine the assumption in experiments. Besides, to address the joint learning task, we propose a novel Dual-LSTM Chain Model to couple response ranking and next utterance suggestion simultaneously. From the experimental results, we demonstrate the usefulness of the proposed task and the effectiveness of the proposed model.

Original languageEnglish (US)
Title of host publicationSIGIR 2017 - Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval
PublisherAssociation for Computing Machinery, Inc
Pages685-694
Number of pages10
ISBN (Electronic)9781450350228
DOIs
StatePublished - Aug 7 2017
Externally publishedYes
Event40th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2017 - Tokyo, Shinjuku, Japan
Duration: Aug 7 2017Aug 11 2017

Publication series

NameSIGIR 2017 - Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval

Other

Other40th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2017
CountryJapan
CityTokyo, Shinjuku
Period8/7/178/11/17

All Science Journal Classification (ASJC) codes

  • Information Systems
  • Software
  • Computer Graphics and Computer-Aided Design

Keywords

  • Conversation System
  • Joint Learning
  • Neural Networks
  • Next Utterance Suggestion
  • Response Ranking

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  • Cite this

    Yan, R., Zhao, D., & Weinan, E. (2017). Joint Learning of response ranking and next utterance suggestion in human-computer conversation system. In SIGIR 2017 - Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 685-694). (SIGIR 2017 - Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval). Association for Computing Machinery, Inc. https://doi.org/10.1145/3077136.3080843