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App. Res. of Comp. Accepted Paper
Vol. 42, 2025 No. 7

Multi-task generative emotion recognition in conversation based on large language models

Long Yuchen1
Gou Zhinan1,2
Chen Yuxin1
Qin Le1
1. School of Management Sciences & Information Engineering, Hebei University of Economics & Business, Shijiazhuang 050061, China
2. Dept. of Computer Science, Tsinghua University, Beijing 100084, China
DOI: 10.19734/j.issn.1001-3695.2024.12.0486

Abstract

Emotion Recognition in Conversation (ERC) is a key task in dialogue systems research. However, existing models often suffer from overfitting to specific datasets and dialogue patterns due to the complexity of pipeline design, which limits their generalization ability. To address this issue, this study proposes a Multi-task Generative Emotion Recognition in Conversation (M-GERC) model based on large language models. The model introduces two auxiliary tasks based on pre-trained large language models: speaker identification and topic-based emotion prediction. The speaker identification task aims to implicitly model the relationships between conversational roles, helping the model better understand emotional exchanges between different participants. The topic-based emotion prediction task predicts the global theme of the conversation, capturing the potential connection between topics and emotions, thus improving emotion recognition accuracy by incorporating contextual information. Additionally, M-GERC introduces a knowledge retrieval module that retrieves domain-specific knowledge and integrates external knowledge to further enhance the model's understanding of context. Experimental results show that M-GERC significantly outperforms existing mainstream ERC models, achieving W-F1 improvements of 3.1%, 4.3% and 3.7% on the DailyDialog, MELD and EmoryNLP datasets, respectively.

Foundation Support

河北省自然科学基金资助项目(F2023207003)、河北经贸大学科学研究与发展计划项目(2024YB23)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2024.12.0486
Publish at: Application Research of Computers Accepted Paper, Vol. 42, 2025 No. 7

Publish History

[2025-03-13] Accepted Paper

Cite This Article

龙禹辰, 勾智楠, 陈宇欣, 等. 基于大语言模型的多任务生成式重构对话情绪识别 [J]. 计算机应用研究, 2025, 42 (7). (2025-03-14). https://doi.org/10.19734/j.issn.1001-3695.2024.12.0486. (Long Yuchen, Gou Zhinan, Chen Yuxin, et al. Multi-task generative emotion recognition in conversation based on large language models [J]. Application Research of Computers, 2025, 42 (7). (2025-03-14). https://doi.org/10.19734/j.issn.1001-3695.2024.12.0486. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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