A Neural Generation-based Conversation Model Using Fine-grained Emotion-guide Attention

Zhiheng Zhou; Man Lan; Yuanbin Wu

doi:10.1109/IJCNN.2018.8488988

A Neural Generation-based Conversation Model Using Fine-grained Emotion-guide Attention

Zhiheng Zhou
, Man Lan
, Yuanbin Wu^*

^*Corresponding author for this work

School of Computer Science and Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

10 Scopus citations

Abstract

Human emotion interaction is crucial to social communications. However, existing generation-based conversation systems mainly put emphasis on the content of responses in terms of naturalness, diversity and coherence without consideration of the emotion interaction between conversation. In order to reduce the gap between human-generated and computer-generated responses, in this work we present a human-like Emotional Conversation Generation Model, named ECGM, by imitating human conversation. Specifically, ECGM applies an emotion-guide attention which captures and integrates the emotion of the given post into neural response generation. Comparative experiments evaluated by computerised and manual methods show that our proposed model is capable of generating more human-like emotional responses and relevant content as well.

Original language	English
Title of host publication	2018 International Joint Conference on Neural Networks, IJCNN 2018 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781509060146
DOIs	https://doi.org/10.1109/IJCNN.2018.8488988
State	Published - 10 Oct 2018
Event	2018 International Joint Conference on Neural Networks, IJCNN 2018 - Rio de Janeiro, Brazil Duration: 8 Jul 2018 → 13 Jul 2018

Publication series

Name	Proceedings of the International Joint Conference on Neural Networks
Volume	2018-July

Conference

Conference	2018 International Joint Conference on Neural Networks, IJCNN 2018
Country/Territory	Brazil
City	Rio de Janeiro
Period	8/07/18 → 13/07/18

Access to Document

10.1109/IJCNN.2018.8488988

Cite this

Zhou, Z., Lan, M., & Wu, Y. (2018). A Neural Generation-based Conversation Model Using Fine-grained Emotion-guide Attention. In 2018 International Joint Conference on Neural Networks, IJCNN 2018 - Proceedings Article 8488988 (Proceedings of the International Joint Conference on Neural Networks; Vol. 2018-July). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IJCNN.2018.8488988

@inproceedings{d6366e8dcfe248789f7739db0d2e9303,

title = "A Neural Generation-based Conversation Model Using Fine-grained Emotion-guide Attention",

abstract = "Human emotion interaction is crucial to social communications. However, existing generation-based conversation systems mainly put emphasis on the content of responses in terms of naturalness, diversity and coherence without consideration of the emotion interaction between conversation. In order to reduce the gap between human-generated and computer-generated responses, in this work we present a human-like Emotional Conversation Generation Model, named ECGM, by imitating human conversation. Specifically, ECGM applies an emotion-guide attention which captures and integrates the emotion of the given post into neural response generation. Comparative experiments evaluated by computerised and manual methods show that our proposed model is capable of generating more human-like emotional responses and relevant content as well.",

author = "Zhiheng Zhou and Man Lan and Yuanbin Wu",

note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 2018 International Joint Conference on Neural Networks, IJCNN 2018 ; Conference date: 08-07-2018 Through 13-07-2018",

year = "2018",

month = oct,

day = "10",

doi = "10.1109/IJCNN.2018.8488988",

language = "英语",

series = "Proceedings of the International Joint Conference on Neural Networks",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "2018 International Joint Conference on Neural Networks, IJCNN 2018 - Proceedings",

address = "美国",

}

Zhou, Z, Lan, M & Wu, Y 2018, A Neural Generation-based Conversation Model Using Fine-grained Emotion-guide Attention. in 2018 International Joint Conference on Neural Networks, IJCNN 2018 - Proceedings., 8488988, Proceedings of the International Joint Conference on Neural Networks, vol. 2018-July, Institute of Electrical and Electronics Engineers Inc., 2018 International Joint Conference on Neural Networks, IJCNN 2018, Rio de Janeiro, Brazil, 8/07/18. https://doi.org/10.1109/IJCNN.2018.8488988

A Neural Generation-based Conversation Model Using Fine-grained Emotion-guide Attention. / Zhou, Zhiheng; Lan, Man; Wu, Yuanbin.
2018 International Joint Conference on Neural Networks, IJCNN 2018 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2018. 8488988 (Proceedings of the International Joint Conference on Neural Networks; Vol. 2018-July).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Lan, Man

AU - Wu, Yuanbin

PY - 2018/10/10

Y1 - 2018/10/10

N2 - Human emotion interaction is crucial to social communications. However, existing generation-based conversation systems mainly put emphasis on the content of responses in terms of naturalness, diversity and coherence without consideration of the emotion interaction between conversation. In order to reduce the gap between human-generated and computer-generated responses, in this work we present a human-like Emotional Conversation Generation Model, named ECGM, by imitating human conversation. Specifically, ECGM applies an emotion-guide attention which captures and integrates the emotion of the given post into neural response generation. Comparative experiments evaluated by computerised and manual methods show that our proposed model is capable of generating more human-like emotional responses and relevant content as well.

AB - Human emotion interaction is crucial to social communications. However, existing generation-based conversation systems mainly put emphasis on the content of responses in terms of naturalness, diversity and coherence without consideration of the emotion interaction between conversation. In order to reduce the gap between human-generated and computer-generated responses, in this work we present a human-like Emotional Conversation Generation Model, named ECGM, by imitating human conversation. Specifically, ECGM applies an emotion-guide attention which captures and integrates the emotion of the given post into neural response generation. Comparative experiments evaluated by computerised and manual methods show that our proposed model is capable of generating more human-like emotional responses and relevant content as well.

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U2 - 10.1109/IJCNN.2018.8488988

DO - 10.1109/IJCNN.2018.8488988

M3 - 会议稿件

AN - SCOPUS:85056524145

T3 - Proceedings of the International Joint Conference on Neural Networks

BT - 2018 International Joint Conference on Neural Networks, IJCNN 2018 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2018 International Joint Conference on Neural Networks, IJCNN 2018

Y2 - 8 July 2018 through 13 July 2018

ER -

A Neural Generation-based Conversation Model Using Fine-grained Emotion-guide Attention

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