Prompt-based Zero-shot Text Classification with Conceptual Knowledge

Yuqi Wang, Wei Wang, Qi Chen, Kaizhu Huang, Anh Nguyen, Suparna De

Abstract
In recent years, pre-trained language models have garnered significant attention due to their effectiveness, which stems from the rich knowledge acquired during pre-training. To mitigate the inconsistency issues between pre-training tasks and downstream tasks and to facilitate the resolution of language-related issues, prompt-based approaches have been introduced, which are particularly useful in low-resource scenarios. However, existing approaches mostly rely on verbalizers to translate the predicted vocabulary to task-specific labels. The major limitations of this approach are the ignorance of potentially relevant domain-specific words and being biased by the pre-training data. To address these limitations, we propose a framework that incorporates conceptual knowledge for text classification in the extreme zero-shot setting. The framework includes prompt-based keyword extraction, weight assignment to each prompt keyword, and final representation estimation in the knowledge graph embedding space. We evaluated the method on four widely-used datasets for sentiment analysis and topic detection, demonstrating that it consistently outperforms recently-developed prompt-based approaches in the same experimental settings.
Anthology ID:
2023.acl-srw.4
Volume:
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop)
Month:
July
Year:
2023
Address:
Toronto, Canada
Editors:
Vishakh Padmakumar, Gisela Vallejo, Yao Fu
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
30–38
Language:
URL:
https://aclanthology.org/2023.acl-srw.4
DOI:
10.18653/v1/2023.acl-srw.4
Bibkey:
Cite (ACL):
Yuqi Wang, Wei Wang, Qi Chen, Kaizhu Huang, Anh Nguyen, and Suparna De. 2023. Prompt-based Zero-shot Text Classification with Conceptual Knowledge. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop), pages 30–38, Toronto, Canada. Association for Computational Linguistics.
Cite (Informal):
Prompt-based Zero-shot Text Classification with Conceptual Knowledge (Wang et al., ACL 2023)
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PDF:
https://preview.aclanthology.org/dois-2013-emnlp/2023.acl-srw.4.pdf
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