ID | 118259 |
著者 |
Zhou, Yangyang
Tokushima University
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キーワード | Affective Computing
Textual emotion detection
Multi-label classification
Prompting Method
Consistency training strategy
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資料タイプ |
学術雑誌論文
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抄録 | 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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掲載誌名 |
IEEE Transactions on Affective Computing
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ISSN | 19493045
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出版者 | IEEE
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巻 | 15
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号 | 1
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開始ページ | 121
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終了ページ | 129
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発行日 | 2023-03-10
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備考 | 論文本文は2025-03-10以降公開予定
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権利情報 | © 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 | |
出版社版DOI | |
出版社版URL | |
言語 |
eng
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著者版フラグ |
その他
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部局 |
理工学系
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