TART: Improved Few-shot Text Classification Using Task-Adaptive Reference Transformation
Shuo Lei, Xuchao Zhang, Jianfeng He, Fanglan Chen, Chang-Tien Lu
Abstract
Meta-learning has emerged as a trending technique to tackle few-shot text classification and achieve state-of-the-art performance. However, the performance of existing approaches heavily depends on the inter-class variance of the support set. As a result, it can perform well on tasks when the semantics of sampled classes are distinct while failing to differentiate classes with similar semantics. In this paper, we propose a novel Task-Adaptive Reference Transformation (TART) network, aiming to enhance the generalization by transforming the class prototypes to per-class fixed reference points in task-adaptive metric spaces. To further maximize divergence between transformed prototypes in task-adaptive metric spaces, TART introduces a discriminative reference regularization among transformed prototypes. Extensive experiments are conducted on four benchmark datasets and our method demonstrates clear superiority over the state-of-the-art models in all the datasets. In particular, our model surpasses the state-of-the-art method by 7.4% and 5.4% in 1-shot and 5-shot classification on the 20 Newsgroups dataset, respectively.- Anthology ID:
- 2023.acl-long.617
- Volume:
- Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long 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:
- 11014–11026
- Language:
- URL:
- https://aclanthology.org/2023.acl-long.617
- DOI:
- 10.18653/v1/2023.acl-long.617
- Cite (ACL):
- Shuo Lei, Xuchao Zhang, Jianfeng He, Fanglan Chen, and Chang-Tien Lu. 2023. TART: Improved Few-shot Text Classification Using Task-Adaptive Reference Transformation. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 11014–11026, Toronto, Canada. Association for Computational Linguistics.
- Cite (Informal):
- TART: Improved Few-shot Text Classification Using Task-Adaptive Reference Transformation (Lei et al., ACL 2023)
- PDF:
- https://preview.aclanthology.org/proper-vol2-ingestion/2023.acl-long.617.pdf