Abstract
Few-shot text classification aims to classify inputs whose label has only a few examples. Previous studies overlooked the semantic relevance between label representations. Therefore, they are easily confused by labels that are relevant. To address this problem, we propose a method that generates distinct label representations that embed information specific to each label. Our method is applicable to conventional few-shot classification models. Experimental results show that our method significantly improved the performance of few-shot text classification across models and datasets.- Anthology ID:
- 2021.acl-short.105
- Volume:
- Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)
- Month:
- August
- Year:
- 2021
- Address:
- Online
- Editors:
- Chengqing Zong, Fei Xia, Wenjie Li, Roberto Navigli
- Venues:
- ACL | IJCNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 831–836
- Language:
- URL:
- https://aclanthology.org/2021.acl-short.105
- DOI:
- 10.18653/v1/2021.acl-short.105
- Cite (ACL):
- Sora Ohashi, Junya Takayama, Tomoyuki Kajiwara, and Yuki Arase. 2021. Distinct Label Representations for Few-Shot Text Classification. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers), pages 831–836, Online. Association for Computational Linguistics.
- Cite (Informal):
- Distinct Label Representations for Few-Shot Text Classification (Ohashi et al., ACL-IJCNLP 2021)
- PDF:
- https://preview.aclanthology.org/dois-2013-emnlp/2021.acl-short.105.pdf
- Code
- 21335732529sky/difference_extractor
- Data
- FewRel