@inproceedings{su-etal-2022-tacl,
title = "{T}a{CL}: Improving {BERT} Pre-training with Token-aware Contrastive Learning",
author = "Su, Yixuan and
Liu, Fangyu and
Meng, Zaiqiao and
Lan, Tian and
Shu, Lei and
Shareghi, Ehsan and
Collier, Nigel",
editor = "Carpuat, Marine and
de Marneffe, Marie-Catherine and
Meza Ruiz, Ivan Vladimir",
booktitle = "Findings of the Association for Computational Linguistics: NAACL 2022",
month = jul,
year = "2022",
address = "Seattle, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.findings-naacl.191",
doi = "10.18653/v1/2022.findings-naacl.191",
pages = "2497--2507",
abstract = "Masked language models (MLMs) such as BERT have revolutionized the field of Natural Language Understanding in the past few years. However, existing pre-trained MLMs often output an anisotropic distribution of token representations that occupies a narrow subset of the entire representation space. Such token representations are not ideal, especially for tasks that demand discriminative semantic meanings of distinct tokens. In this work, we propose TaCL (Token-aware Contrastive Learning), a novel continual pre-training approach that encourages BERT to learn an isotropic and discriminative distribution of token representations. TaCL is fully unsupervised and requires no additional data. We extensively test our approach on a wide range of English and Chinese benchmarks. The results show that TaCL brings consistent and notable improvements over the original BERT model. Furthermore, we conduct detailed analysis to reveal the merits and inner-workings of our approach.",
}
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<abstract>Masked language models (MLMs) such as BERT have revolutionized the field of Natural Language Understanding in the past few years. However, existing pre-trained MLMs often output an anisotropic distribution of token representations that occupies a narrow subset of the entire representation space. Such token representations are not ideal, especially for tasks that demand discriminative semantic meanings of distinct tokens. In this work, we propose TaCL (Token-aware Contrastive Learning), a novel continual pre-training approach that encourages BERT to learn an isotropic and discriminative distribution of token representations. TaCL is fully unsupervised and requires no additional data. We extensively test our approach on a wide range of English and Chinese benchmarks. The results show that TaCL brings consistent and notable improvements over the original BERT model. Furthermore, we conduct detailed analysis to reveal the merits and inner-workings of our approach.</abstract>
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%0 Conference Proceedings
%T TaCL: Improving BERT Pre-training with Token-aware Contrastive Learning
%A Su, Yixuan
%A Liu, Fangyu
%A Meng, Zaiqiao
%A Lan, Tian
%A Shu, Lei
%A Shareghi, Ehsan
%A Collier, Nigel
%Y Carpuat, Marine
%Y de Marneffe, Marie-Catherine
%Y Meza Ruiz, Ivan Vladimir
%S Findings of the Association for Computational Linguistics: NAACL 2022
%D 2022
%8 July
%I Association for Computational Linguistics
%C Seattle, United States
%F su-etal-2022-tacl
%X Masked language models (MLMs) such as BERT have revolutionized the field of Natural Language Understanding in the past few years. However, existing pre-trained MLMs often output an anisotropic distribution of token representations that occupies a narrow subset of the entire representation space. Such token representations are not ideal, especially for tasks that demand discriminative semantic meanings of distinct tokens. In this work, we propose TaCL (Token-aware Contrastive Learning), a novel continual pre-training approach that encourages BERT to learn an isotropic and discriminative distribution of token representations. TaCL is fully unsupervised and requires no additional data. We extensively test our approach on a wide range of English and Chinese benchmarks. The results show that TaCL brings consistent and notable improvements over the original BERT model. Furthermore, we conduct detailed analysis to reveal the merits and inner-workings of our approach.
%R 10.18653/v1/2022.findings-naacl.191
%U https://aclanthology.org/2022.findings-naacl.191
%U https://doi.org/10.18653/v1/2022.findings-naacl.191
%P 2497-2507
Markdown (Informal)
[TaCL: Improving BERT Pre-training with Token-aware Contrastive Learning](https://aclanthology.org/2022.findings-naacl.191) (Su et al., Findings 2022)
ACL