MLAL: Multiple Prompt Learning and Generation of Auxiliary Labeled Utterances for Emotion Recognition in Conversations
Emotion Recognition in Conversations (ERC) is one of the most prominent research directions in the field of Natural Language Processing (NLP). It aims to accurately identify the emotional state expressed in conversations and is widely applied in psychology, education, and healthcare. However, ERC po...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-06-01
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| Series: | Machine Learning with Applications |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S266682702500026X |
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| author | Zhinan Gou Yuxin Chen Yuchen Long Mengyao Jia Zhili Liu Jun Zhu |
| author_facet | Zhinan Gou Yuxin Chen Yuchen Long Mengyao Jia Zhili Liu Jun Zhu |
| author_sort | Zhinan Gou |
| collection | DOAJ |
| description | Emotion Recognition in Conversations (ERC) is one of the most prominent research directions in the field of Natural Language Processing (NLP). It aims to accurately identify the emotional state expressed in conversations and is widely applied in psychology, education, and healthcare. However, ERC poses significant challenges due to various factors, such as conversational context, the experience of speaker, and subtle differences between similar emotion labels. Existing research primarily strives for effective sequence and graph structure to model utterance and interaction. Moreover, these methods lack comprehensive understanding of conversational contexts and precise distinction between similar emotions. To address the limitation, in this study, we propose a novel framework combining Multiple Prompt Learning and Generation of Auxiliary Labeled Utterances (MLAL). Firstly, a global prompt is constructed to facilitate the understanding of the conversational context. Specifically, utterances originating from the same speaker are identified and interactively processed. Simultaneously, taking into account the influence of speaker experience, an experience prompt is designed by retrieving and interacting with the historical utterances of speakers that display high similarity. Besides, we generate refined auxiliary labeled utterances by means of the label paraphrasing mechanism to distinguish between similar emotions. Results from experiments show that our proposed approach performs better on three datasets than the state-of-the-art techniques currently in use. |
| format | Article |
| id | doaj-art-ddf74f86b2b1464384ce7e11243942f7 |
| institution | DOAJ |
| issn | 2666-8270 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Machine Learning with Applications |
| spelling | doaj-art-ddf74f86b2b1464384ce7e11243942f72025-08-20T03:21:01ZengElsevierMachine Learning with Applications2666-82702025-06-012010064310.1016/j.mlwa.2025.100643MLAL: Multiple Prompt Learning and Generation of Auxiliary Labeled Utterances for Emotion Recognition in ConversationsZhinan Gou0Yuxin Chen1Yuchen Long2Mengyao Jia3Zhili Liu4Jun Zhu5School of Management Science and Information Engineering, Hebei University of Economics and Business, Shijiazhuang 050061, China; Corresponding author.School of Management Science and Information Engineering, Hebei University of Economics and Business, Shijiazhuang 050061, ChinaSchool of Management Science and Information Engineering, Hebei University of Economics and Business, Shijiazhuang 050061, ChinaSchool of Management Science and Information Engineering, Hebei University of Economics and Business, Shijiazhuang 050061, ChinaCollege of Business Administration, Hebei University of Economics and Business, Shijiazhuang 050061, ChinaSchool of Computer and Software, Nanjing Vocational University of Industry Technology, Nanjing 210023, ChinaEmotion Recognition in Conversations (ERC) is one of the most prominent research directions in the field of Natural Language Processing (NLP). It aims to accurately identify the emotional state expressed in conversations and is widely applied in psychology, education, and healthcare. However, ERC poses significant challenges due to various factors, such as conversational context, the experience of speaker, and subtle differences between similar emotion labels. Existing research primarily strives for effective sequence and graph structure to model utterance and interaction. Moreover, these methods lack comprehensive understanding of conversational contexts and precise distinction between similar emotions. To address the limitation, in this study, we propose a novel framework combining Multiple Prompt Learning and Generation of Auxiliary Labeled Utterances (MLAL). Firstly, a global prompt is constructed to facilitate the understanding of the conversational context. Specifically, utterances originating from the same speaker are identified and interactively processed. Simultaneously, taking into account the influence of speaker experience, an experience prompt is designed by retrieving and interacting with the historical utterances of speakers that display high similarity. Besides, we generate refined auxiliary labeled utterances by means of the label paraphrasing mechanism to distinguish between similar emotions. Results from experiments show that our proposed approach performs better on three datasets than the state-of-the-art techniques currently in use.http://www.sciencedirect.com/science/article/pii/S266682702500026XEmotion recognition in conversationsGlobal promptExperience promptGeneration of auxiliary labeled utterances |
| spellingShingle | Zhinan Gou Yuxin Chen Yuchen Long Mengyao Jia Zhili Liu Jun Zhu MLAL: Multiple Prompt Learning and Generation of Auxiliary Labeled Utterances for Emotion Recognition in Conversations Machine Learning with Applications Emotion recognition in conversations Global prompt Experience prompt Generation of auxiliary labeled utterances |
| title | MLAL: Multiple Prompt Learning and Generation of Auxiliary Labeled Utterances for Emotion Recognition in Conversations |
| title_full | MLAL: Multiple Prompt Learning and Generation of Auxiliary Labeled Utterances for Emotion Recognition in Conversations |
| title_fullStr | MLAL: Multiple Prompt Learning and Generation of Auxiliary Labeled Utterances for Emotion Recognition in Conversations |
| title_full_unstemmed | MLAL: Multiple Prompt Learning and Generation of Auxiliary Labeled Utterances for Emotion Recognition in Conversations |
| title_short | MLAL: Multiple Prompt Learning and Generation of Auxiliary Labeled Utterances for Emotion Recognition in Conversations |
| title_sort | mlal multiple prompt learning and generation of auxiliary labeled utterances for emotion recognition in conversations |
| topic | Emotion recognition in conversations Global prompt Experience prompt Generation of auxiliary labeled utterances |
| url | http://www.sciencedirect.com/science/article/pii/S266682702500026X |
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