An Empirical Study on Multiple Knowledge from ChatGPT for Emotion Recognition in Conversations

Geng Tu; Bin Liang; Bing Qin; Kam Fai Wong; Ruifeng Xu

doi:10.18653/v1/2023.findings-emnlp.813

An Empirical Study on Multiple Knowledge from ChatGPT for Emotion Recognition in Conversations

Geng Tu
, Bin Liang^*
, Bing Qin
, Kam Fai Wong
, Ruifeng Xu^*

^*Corresponding author for this work

Faculty of Computing

Harbin Institute of Technology Shenzhen
Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies
Chinese University of Hong Kong
Peng Cheng Laboratory

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

22 Scopus citations

Abstract

Multiple knowledge (e.g., co-reference, topics, emotional causes, etc) has been demonstrated effective for emotion detection. However, exploring this knowledge in Emotion Recognition in Conversations (ERC) is currently a blank slate due to the lack of annotated data and the high cost involved in obtaining such knowledge. Fortunately, the emergence of Large Language Models (LLMs) holds promise in filling this void. Therefore, we propose a Multiple Knowledge Fusion Model (MKFM) to effectively integrate such knowledge generated by LLMs for ERC and empirically study its impact on the model. Experimental results on three public datasets have demonstrated the effectiveness of multiple knowledge for ERC. Furthermore, we conduct a detailed analysis of the contribution and complementarity of this knowledge.

Original language	English
Title of host publication	Findings of the Association for Computational Linguistics
Subtitle of host publication	EMNLP 2023
Publisher	Association for Computational Linguistics (ACL)
Pages	12160-12173
Number of pages	14
ISBN (Electronic)	9798891760615
DOIs	https://doi.org/10.18653/v1/2023.findings-emnlp.813
State	Published - 2023
Event	2023 Findings of the Association for Computational Linguistics: EMNLP 2023 - Hybrid, Singapore Duration: 6 Dec 2023 → 10 Dec 2023

Publication series

Name	Findings of the Association for Computational Linguistics: EMNLP 2023

Conference

Conference	2023 Findings of the Association for Computational Linguistics: EMNLP 2023
Country/Territory	Singapore
City	Hybrid
Period	6/12/23 → 10/12/23

Access to Document

10.18653/v1/2023.findings-emnlp.813

Cite this

Tu, G., Liang, B., Qin, B., Wong, K. F., & Xu, R. (2023). An Empirical Study on Multiple Knowledge from ChatGPT for Emotion Recognition in Conversations. In Findings of the Association for Computational Linguistics: EMNLP 2023 (pp. 12160-12173). (Findings of the Association for Computational Linguistics: EMNLP 2023). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/2023.findings-emnlp.813

@inproceedings{bc6a95375f4047a59e88e3d4e80110cd,

title = "An Empirical Study on Multiple Knowledge from ChatGPT for Emotion Recognition in Conversations",

abstract = "Multiple knowledge (e.g., co-reference, topics, emotional causes, etc) has been demonstrated effective for emotion detection. However, exploring this knowledge in Emotion Recognition in Conversations (ERC) is currently a blank slate due to the lack of annotated data and the high cost involved in obtaining such knowledge. Fortunately, the emergence of Large Language Models (LLMs) holds promise in filling this void. Therefore, we propose a Multiple Knowledge Fusion Model (MKFM) to effectively integrate such knowledge generated by LLMs for ERC and empirically study its impact on the model. Experimental results on three public datasets have demonstrated the effectiveness of multiple knowledge for ERC. Furthermore, we conduct a detailed analysis of the contribution and complementarity of this knowledge.",

author = "Geng Tu and Bin Liang and Bing Qin and Wong, \{Kam Fai\} and Ruifeng Xu",

note = "Publisher Copyright: {\textcopyright} 2023 Association for Computational Linguistics.; 2023 Findings of the Association for Computational Linguistics: EMNLP 2023 ; Conference date: 06-12-2023 Through 10-12-2023",

year = "2023",

doi = "10.18653/v1/2023.findings-emnlp.813",

language = "英语",

series = "Findings of the Association for Computational Linguistics: EMNLP 2023",

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}

Tu, G, Liang, B, Qin, B, Wong, KF & Xu, R 2023, An Empirical Study on Multiple Knowledge from ChatGPT for Emotion Recognition in Conversations. in Findings of the Association for Computational Linguistics: EMNLP 2023. Findings of the Association for Computational Linguistics: EMNLP 2023, Association for Computational Linguistics (ACL), pp. 12160-12173, 2023 Findings of the Association for Computational Linguistics: EMNLP 2023, Hybrid, Singapore, 6/12/23. https://doi.org/10.18653/v1/2023.findings-emnlp.813

An Empirical Study on Multiple Knowledge from ChatGPT for Emotion Recognition in Conversations. / Tu, Geng; Liang, Bin; Qin, Bing et al.
Findings of the Association for Computational Linguistics: EMNLP 2023. Association for Computational Linguistics (ACL), 2023. p. 12160-12173 (Findings of the Association for Computational Linguistics: EMNLP 2023).

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

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AU - Xu, Ruifeng

PY - 2023

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AB - Multiple knowledge (e.g., co-reference, topics, emotional causes, etc) has been demonstrated effective for emotion detection. However, exploring this knowledge in Emotion Recognition in Conversations (ERC) is currently a blank slate due to the lack of annotated data and the high cost involved in obtaining such knowledge. Fortunately, the emergence of Large Language Models (LLMs) holds promise in filling this void. Therefore, we propose a Multiple Knowledge Fusion Model (MKFM) to effectively integrate such knowledge generated by LLMs for ERC and empirically study its impact on the model. Experimental results on three public datasets have demonstrated the effectiveness of multiple knowledge for ERC. Furthermore, we conduct a detailed analysis of the contribution and complementarity of this knowledge.

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Tu G, Liang B, Qin B, Wong KF, Xu R. An Empirical Study on Multiple Knowledge from ChatGPT for Emotion Recognition in Conversations. In Findings of the Association for Computational Linguistics: EMNLP 2023. Association for Computational Linguistics (ACL). 2023. p. 12160-12173. (Findings of the Association for Computational Linguistics: EMNLP 2023). doi: 10.18653/v1/2023.findings-emnlp.813

An Empirical Study on Multiple Knowledge from ChatGPT for Emotion Recognition in Conversations

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