Abstract
Traditional text classification typically categorizes texts into pre-defined coarse-grained classes, from which the produced models cannot handle the real-world scenario where finer categories emerge periodically for accurate services. In this work, we investigate the setting where fine-grained classification is done only using the annotation of coarse-grained categories and the coarse-to-fine mapping. We propose a lightweight contrastive clustering-based bootstrapping method to iteratively refine the labels of passages. During clustering, it pulls away negative passage-prototype pairs under the guidance of the mapping from both global and local perspectives. Experiments on NYT and 20News show that our method outperforms the state-of-the-art methods by a large margin.- Anthology ID:
- 2023.acl-short.84
- Volume:
- Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
- Month:
- July
- Year:
- 2023
- Address:
- Toronto, Canada
- Editors:
- Anna Rogers, Jordan Boyd-Graber, Naoaki Okazaki
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 976–985
- Language:
- URL:
- https://aclanthology.org/2023.acl-short.84
- DOI:
- 10.18653/v1/2023.acl-short.84
- Cite (ACL):
- Shudi Hou, Yu Xia, Muhao Chen, and Sujian Li. 2023. Contrastive Bootstrapping for Label Refinement. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 976–985, Toronto, Canada. Association for Computational Linguistics.
- Cite (Informal):
- Contrastive Bootstrapping for Label Refinement (Hou et al., ACL 2023)
- PDF:
- https://preview.aclanthology.org/improve-issue-templates/2023.acl-short.84.pdf