@inproceedings{b7db3896136d4112afadee23e8e64e8d,
title = "PromptBERT: Improving BERT Sentence Embeddings with Prompts",
abstract = "We propose PromptBERT, a novel contrastive learning method for learning better sentence representation. We firstly analyze 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. Our code is available at https://github.com/kongds/Prompt-BERT.",
author = "Ting Jiang and Jian Jiao and Shaohan Huang and Zihan Zhang and Deqing Wang and Fuzhen Zhuang and Furu Wei and Haizhen Huang and Denvy Deng and Qi Zhang",
note = "Publisher Copyright: {\textcopyright} 2022 Association for Computational Linguistics.; 2022 Conference on Empirical Methods in Natural Language Processing, EMNLP 2022 ; Conference date: 07-12-2022 Through 11-12-2022",
year = "2022",
doi = "10.18653/v1/2022.emnlp-main.603",
language = "英语",
series = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, EMNLP 2022",
publisher = "Association for Computational Linguistics (ACL)",
pages = "8826--8837",
editor = "Yoav Goldberg and Zornitsa Kozareva and Yue Zhang",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, EMNLP 2022",
address = "澳大利亚",
}