Label Anchored Contrastive Learning for Language Understanding

Zhenyu Zhang, Yuming Zhao, Meng Chen, Xiaodong He


Abstract
Contrastive learning (CL) has achieved astonishing progress in computer vision, speech, and natural language processing fields recently with self-supervised learning. However, CL approach to the supervised setting is not fully explored, especially for the natural language understanding classification task. Intuitively, the class label itself has the intrinsic ability to perform hard positive/negative mining, which is crucial for CL. Motivated by this, we propose a novel label anchored contrastive learning approach (denoted as LaCon) for language understanding. Specifically, three contrastive objectives are devised, including a multi-head instance-centered contrastive loss (ICL), a label-centered contrastive loss (LCL), and a label embedding regularizer (LER). Our approach does not require any specialized network architecture or any extra data augmentation, thus it can be easily plugged into existing powerful pre-trained language models. Compared to the state-of-the-art baselines, LaCon obtains up to 4.1% improvement on the popular datasets of GLUE and CLUE benchmarks. Besides, LaCon also demonstrates significant advantages under the few-shot and data imbalance settings, which obtains up to 9.4% improvement on the FewGLUE and FewCLUE benchmarking tasks.
Anthology ID:
2022.naacl-main.103
Volume:
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Month:
July
Year:
2022
Address:
Seattle, United States
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1437–1449
Language:
URL:
https://aclanthology.org/2022.naacl-main.103
DOI:
10.18653/v1/2022.naacl-main.103
Bibkey:
Cite (ACL):
Zhenyu Zhang, Yuming Zhao, Meng Chen, and Xiaodong He. 2022. Label Anchored Contrastive Learning for Language Understanding. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 1437–1449, Seattle, United States. Association for Computational Linguistics.
Cite (Informal):
Label Anchored Contrastive Learning for Language Understanding (Zhang et al., NAACL 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.naacl-main.103.pdf
Data
CLUEFewCLUEFewGlueGLUEQNLI