ID | 118259 |
Author |
Zhou, Yangyang
Tokushima University
Ren, Fuji
Tokushima University
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Keywords | Affective Computing
Textual emotion detection
Multi-label classification
Prompting Method
Consistency training strategy
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Content Type |
Journal Article
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Description | Textual emotion detection is playing an important role in the human-computer interaction domain. The mainstream methods of textual emotion detection are extracting semantic features and fine-tuning by language models. Due to the information redundancy in semantics, it is difficult for these methods to accurately detect all the emotions implied in the text. The prompting method has been shown to make the language models more purposeful in prediction by filling the cloze or prefix prompts defined. Therefore, we design a prompting method for multi-label classification. To stabilize the output, we design two consistency training strategies. We experiment on two multi-label emotion classification datasets: Ren-CECps and NLPCC2018. Our proposed prompting method with consistency training strategies for multi-label textual emotion detection (PC-MTED) model achieves state-of-the-art Macro F1 scores of 0.5432 and 0.5269, respectively. The experimental results indicate that our proposed method is effective in the multi-label textual emotion detection task.
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Journal Title |
IEEE Transactions on Affective Computing
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ISSN | 19493045
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Publisher | IEEE
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Volume | 15
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Issue | 1
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Start Page | 121
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End Page | 129
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Published Date | 2023-03-10
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Remark | 論文本文は2025-03-10以降公開予定
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Rights | © 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
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EDB ID | |
DOI (Published Version) | |
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language |
eng
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TextVersion |
その他
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departments |
Science and Technology
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