PromptBERT: Improving BERT Sentence Embeddings with Prompts

Ting Jiang, Jian Jiao, Shaohan Huang, Zihan Zhang, Deqing Wang, Fuzhen Zhuang, Furu Wei, Haizhen Huang, Denvy Deng, Qi Zhang


Abstract
We propose PromptBERT, a novel contrastive learning method for learning better sentence representation. We firstly analysis the drawback of current sentence embedding from original BERT and find that it is mainly due to the static token embedding bias and ineffective BERT layers. Then we propose the first prompt-based sentence embeddings method and discuss two prompt representing methods and three prompt searching methods to make BERT achieve better sentence embeddings .Moreover, we propose a novel unsupervised training objective by the technology of template denoising, which substantially shortens the performance gap between the supervised and unsupervised settings. Extensive experiments show the effectiveness of our method. Compared to SimCSE, PromptBert achieves 2.29 and 2.58 points of improvement based on BERT and RoBERTa in the unsupervised setting.
Anthology ID:
2022.emnlp-main.603
Volume:
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
Month:
December
Year:
2022
Address:
Abu Dhabi, United Arab Emirates
Editors:
Yoav Goldberg, Zornitsa Kozareva, Yue Zhang
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
8826–8837
Language:
URL:
https://aclanthology.org/2022.emnlp-main.603
DOI:
10.18653/v1/2022.emnlp-main.603
Bibkey:
Cite (ACL):
Ting Jiang, Jian Jiao, Shaohan Huang, Zihan Zhang, Deqing Wang, Fuzhen Zhuang, Furu Wei, Haizhen Huang, Denvy Deng, and Qi Zhang. 2022. PromptBERT: Improving BERT Sentence Embeddings with Prompts. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 8826–8837, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
Cite (Informal):
PromptBERT: Improving BERT Sentence Embeddings with Prompts (Jiang et al., EMNLP 2022)
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PDF:
https://preview.aclanthology.org/ingest-acl-2023-videos/2022.emnlp-main.603.pdf