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Prompt Consistency for Multi-label Textual Emotion Detection
https://tokushima-u.repo.nii.ac.jp/records/2011021
https://tokushima-u.repo.nii.ac.jp/records/20110212b5ca674-eda4-4f29-99e6-66d534f4a932
| 名前 / ファイル | ライセンス | アクション |
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taffc_15_1_121.pdf (4.69 MB)
Download is available from 2025/3/10.
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| Item type | 文献 / Documents(1) | |||||||||||||
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| 公開日 | 2023-09-27 | |||||||||||||
| アクセス権 | ||||||||||||||
| アクセス権 | embargoed access | |||||||||||||
| 資源タイプ | ||||||||||||||
| 資源タイプ識別子 | http://purl.org/coar/resource_type/c_6501 | |||||||||||||
| 資源タイプ | journal article | |||||||||||||
| 出版社版DOI | ||||||||||||||
| 識別子タイプ | DOI | |||||||||||||
| 関連識別子 | https://doi.org/10.1109/TAFFC.2023.3254883 | |||||||||||||
| 言語 | ja | |||||||||||||
| 関連名称 | 10.1109/TAFFC.2023.3254883 | |||||||||||||
| 出版タイプ | ||||||||||||||
| 出版タイプ | NA | |||||||||||||
| 出版タイプResource | http://purl.org/coar/version/c_be7fb7dd8ff6fe43 | |||||||||||||
| タイトル | ||||||||||||||
| タイトル | Prompt Consistency for Multi-label Textual Emotion Detection | |||||||||||||
| 言語 | en | |||||||||||||
| 著者 |
Zhou, Yangyang
× Zhou, Yangyang
× 康, 鑫× 任, 福継
WEKO
401
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| 抄録 | ||||||||||||||
| 内容記述タイプ | Abstract | |||||||||||||
| 内容記述 | 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. | |||||||||||||
| 言語 | en | |||||||||||||
| キーワード | ||||||||||||||
| 言語 | en | |||||||||||||
| 主題Scheme | Other | |||||||||||||
| 主題 | Affective Computing | |||||||||||||
| キーワード | ||||||||||||||
| 言語 | en | |||||||||||||
| 主題Scheme | Other | |||||||||||||
| 主題 | Textual emotion detection | |||||||||||||
| キーワード | ||||||||||||||
| 言語 | en | |||||||||||||
| 主題Scheme | Other | |||||||||||||
| 主題 | Multi-label classification | |||||||||||||
| キーワード | ||||||||||||||
| 言語 | en | |||||||||||||
| 主題Scheme | Other | |||||||||||||
| 主題 | Prompting Method | |||||||||||||
| キーワード | ||||||||||||||
| 言語 | en | |||||||||||||
| 主題Scheme | Other | |||||||||||||
| 主題 | Consistency training strategy | |||||||||||||
| 書誌情報 |
en : IEEE Transactions on Affective Computing 巻 15, 号 1, p. 121-129, 発行日 2023-03-10 |
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| 収録物ID | ||||||||||||||
| 収録物識別子タイプ | ISSN | |||||||||||||
| 収録物識別子 | 19493045 | |||||||||||||
| 出版者 | ||||||||||||||
| 出版者 | IEEE | |||||||||||||
| 言語 | en | |||||||||||||
| 権利情報 | ||||||||||||||
| 言語 | en | |||||||||||||
| 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. | |||||||||||||
| EID | ||||||||||||||
| 識別子 | 394652 | |||||||||||||
| 識別子タイプ | URI | |||||||||||||
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| 言語 | eng | |||||||||||||
