WhiteningBERT: An Easy Unsupervised Sentence Embedding Approach

Junjie Huang, Duyu Tang, Wanjun Zhong, Shuai Lu, Linjun Shou, Ming Gong, Daxin Jiang, Nan Duan


Abstract
Producing the embedding of a sentence in anunsupervised way is valuable to natural language matching and retrieval problems in practice. In this work, we conduct a thorough examination of pretrained model based unsupervised sentence embeddings. We study on fourpretrained models and conduct massive experiments on seven datasets regarding sentence semantics. We have three main findings. First, averaging all tokens is better than only using [CLS] vector. Second, combining both topand bottom layers is better than only using toplayers. Lastly, an easy whitening-based vector normalization strategy with less than 10 linesof code consistently boosts the performance. The whole project including codes and data is publicly available at https://github.com/Jun-jie-Huang/WhiteningBERT.
Anthology ID:
2021.findings-emnlp.23
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2021
Month:
November
Year:
2021
Address:
Punta Cana, Dominican Republic
Venues:
EMNLP | Findings
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
238–244
Language:
URL:
https://aclanthology.org/2021.findings-emnlp.23
DOI:
10.18653/v1/2021.findings-emnlp.23
Bibkey:
Cite (ACL):
Junjie Huang, Duyu Tang, Wanjun Zhong, Shuai Lu, Linjun Shou, Ming Gong, Daxin Jiang, and Nan Duan. 2021. WhiteningBERT: An Easy Unsupervised Sentence Embedding Approach. In Findings of the Association for Computational Linguistics: EMNLP 2021, pages 238–244, Punta Cana, Dominican Republic. Association for Computational Linguistics.
Cite (Informal):
WhiteningBERT: An Easy Unsupervised Sentence Embedding Approach (Huang et al., Findings 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.findings-emnlp.23.pdf
Code
 Jun-jie-Huang/WhiteningBERT