research-article

Neural Conversation Generation with Auxiliary Emotional Supervised Models

Authors:

Jiaheng LuAuthors Info & Claims

ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP), Volume 19, Issue 2

Article No.: 19, Pages 1 - 17

https://doi.org/10.1145/3344788

Published: 17 September 2019 Publication History

Abstract

An important aspect of developing dialogue agents involves endowing a conversation system with emotion perception and interaction. Most existing emotion dialogue models lack the adaptability and extensibility of different scenes because of their limitation to require a specified emotion category or their reliance on a fixed emotional dictionary. To overcome these limitations, we propose a neural conversation generation with auxiliary emotional supervised model (nCG-ESM) comprising a sequence-to-sequence (Seq2Seq) generation model and an emotional classifier used as an auxiliary model. The emotional classifier was trained to predict the emotion distributions of the dialogues, which were then used as emotion supervised signals to guide the generation model to generate diverse emotional responses. The proposed nCG-ESM is flexible enough to generate responses with emotional diversity, including specified or unspecified emotions, which can be adapted and extended to different scenarios. We conducted extensive experiments on the popular dataset of Weibo post--response pairs. Experimental results showed that the proposed model was capable of producing more diverse, appropriate, and emotionally rich responses, yielding substantial gains in diversity scores and human evaluations.

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Cited By

Su YBian HFan BLian BZhang CZhang BHuang R(2024)RLCA: Reinforcement Learning Model Integrating Cognition and Affection for Empathetic Response GenerationIEEE Transactions on Computational Social Systems10.1109/TCSS.2023.325874111:1(1158-1168)Online publication date: Feb-2024
https://doi.org/10.1109/TCSS.2023.3258741
Castro ECalvo HKolesnikova OCastro C(2023)Analysis of Emotions in Speech Acts for Chatbots: An Overview and a Model Proposal2023 IEEE Symposium Series on Computational Intelligence (SSCI)10.1109/SSCI52147.2023.10371792(1467-1471)Online publication date: 5-Dec-2023
https://doi.org/10.1109/SSCI52147.2023.10371792
Bilquise GIbrahim SShaalan K(2022)Emotionally Intelligent Chatbots: A Systematic Literature ReviewHuman Behavior and Emerging Technologies10.1155/2022/96016302022(1-23)Online publication date: 26-Sep-2022
https://doi.org/10.1155/2022/9601630
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Index Terms

Neural Conversation Generation with Auxiliary Emotional Supervised Models
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing

Index terms have been assigned to the content through auto-classification.

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cover image ACM Transactions on Asian and Low-Resource Language Information Processing

ACM Transactions on Asian and Low-Resource Language Information Processing Volume 19, Issue 2

March 2020

301 pages

ISSN:2375-4699

EISSN:2375-4702

DOI:10.1145/3358605

Editor:
Imed Zitouni
Microsoft, USA

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Copyright © 2019 ACM.

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Association for Computing Machinery

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Publication History

Published: 17 September 2019

Accepted: 01 July 2019

Revised: 01 February 2019

Received: 01 April 2018

Published in TALLIP Volume 19, Issue 2

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National Natural Science Foundation of China
Fundamental Research Funds for the Central Universities

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Cited By

Su YBian HFan BLian BZhang CZhang BHuang R(2024)RLCA: Reinforcement Learning Model Integrating Cognition and Affection for Empathetic Response GenerationIEEE Transactions on Computational Social Systems10.1109/TCSS.2023.325874111:1(1158-1168)Online publication date: Feb-2024
https://doi.org/10.1109/TCSS.2023.3258741
Castro ECalvo HKolesnikova OCastro C(2023)Analysis of Emotions in Speech Acts for Chatbots: An Overview and a Model Proposal2023 IEEE Symposium Series on Computational Intelligence (SSCI)10.1109/SSCI52147.2023.10371792(1467-1471)Online publication date: 5-Dec-2023
https://doi.org/10.1109/SSCI52147.2023.10371792
Bilquise GIbrahim SShaalan K(2022)Emotionally Intelligent Chatbots: A Systematic Literature ReviewHuman Behavior and Emerging Technologies10.1155/2022/96016302022(1-23)Online publication date: 26-Sep-2022
https://doi.org/10.1155/2022/9601630
Ma HWang ZZhou XZhou GZhou Q(2022)Emotion Recognition with Conversational Generation TransferACM Transactions on Asian and Low-Resource Language Information Processing10.1145/349453221:4(1-17)Online publication date: 19-Jan-2022
https://dl.acm.org/doi/10.1145/3494532
Wang JSun XWang M(2022)Emotional Conversation Generation With Bilingual Interactive DecodingIEEE Transactions on Computational Social Systems10.1109/TCSS.2021.30954799:3(818-829)Online publication date: Jun-2022
https://doi.org/10.1109/TCSS.2021.3095479
Yang RMa ZWang CDu B(2022)Enhancing Integrity Modeling for Emotional Conversation GenerationIEEE Transactions on Cognitive and Developmental Systems10.1109/TCDS.2021.309844414:3(1170-1178)Online publication date: Sep-2022
https://doi.org/10.1109/TCDS.2021.3098444
Majumder NGhosal DHazarika DGelbukh AMihalcea RPoria S(2022)Exemplars-Guided Empathetic Response Generation Controlled by the Elements of Human CommunicationIEEE Access10.1109/ACCESS.2022.319315910(77176-77190)Online publication date: 2022
https://doi.org/10.1109/ACCESS.2022.3193159
Ahanin ZIsmail M(2022)A multi-label emoji classification method using balanced pointwise mutual information-based feature selectionComputer Speech and Language10.1016/j.csl.2021.10133073:COnline publication date: 1-May-2022
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Zhang RWang ZHuang ZLi LZheng M(2021)Predicting Emotion Reactions for Human–Computer Conversation: A Variational ApproachIEEE Transactions on Human-Machine Systems10.1109/THMS.2020.304497551:4(279-287)Online publication date: Aug-2021
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Duan MLi QXiao L(2021)Topic-extended Emotional Conversation Generation Model Based on Joint DecodingIEEE Access10.1109/ACCESS.2021.30904359(89934-89940)Online publication date: 2021
https://doi.org/10.1109/ACCESS.2021.3090435
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