基于大语言模型的多任务生成式重构对话情绪识别

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中图分类号：TP391 文献标志码：A 文章编号：1001-3695（2025）07-006-1964-08

doi：10.19734/j. issn. 1001-3695.2024.12.0486

Abstract：Emotionrecognition inconversation（ERC）isakey task indialogue systems research.However，existing models oftensuferfromoverfittng tospecificdatasetsanddialoguepatersduetothecomplexityofpipelinedsign，hichlimitstheir generalizationability.Toaddress thisisse，thisstudyproposedamulti-task generativeemotionrecognitioninconversatin（MGERC）model basedonlarge language models.The model introduced two auxiliarytasksbasedon pre-trained large language models;speakeridentifcationandtopic-basedemotionprediction.Thespeakeridentificationtaskaimed toimplicitlymodelthe relationshipsbetweeconversationaloles，lpingthemodelbeterunderstandmotionalxchangesbetween diffrentparticipants.The topic-based emotionprediction task predicted theglobalthemeoftheconversation，capturing thepotentialconnectionbetweentopicsandemotions，thusimproving emotionrecognitionaccuracybyincorporatingcontextualinformation.Aditionaly，M-GERCintroducedaknowledgeretrieval modulethatretrieveddomain-specificknowledgeandintegratedexteral knowledge to further enhance the model’sunderstanding ofcontext.Experimentalresultsshow that M-GERC significantlyoutperforms existing mainstream ERC models，achieving W-F1 improvements of 3.1% ， 4.3% and 3.7% on the DailyDialog， MELD and EmoryNLP datasets，respectively.

Key Words：emotion recognition in conversation；large language models；topic；external knowledge

0 引言

“如果想要真正的智能机器，那么必须让它们能够理解情绪，而不仅仅是执行特定的任务。（剩余22100字）