@inproceedings{zeng-etal-2022-contrastive,
title = "Contrastive Learning with Prompt-derived Virtual Semantic Prototypes for Unsupervised Sentence Embedding",
author = "Zeng, Jiali and
Yin, Yongjing and
Jiang, Yufan and
Wu, Shuangzhi and
Cao, Yunbo",
editor = "Goldberg, Yoav and
Kozareva, Zornitsa and
Zhang, Yue",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2022",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.findings-emnlp.522",
doi = "10.18653/v1/2022.findings-emnlp.522",
pages = "7042--7053",
abstract = "Contrastive learning has become a new paradigm for unsupervised sentence embeddings.Previous studies focus on instance-wise contrastive learning, attempting to construct positive pairs with textual data augmentation. In this paper, we propose a novel Contrastive learning method with Prompt-derived Virtual semantic Prototypes (ConPVP). Specifically, with the help of prompts, we construct virtual semantic prototypes to each instance, and derive negative prototypes by using the negative form of the prompts. Using a prototypical contrastive loss, we enforce the anchor sentence embedding to be close to its corresponding semantic prototypes, and far apart from the negative prototypes as well as the prototypes of other sentences. Extensive experimental results on semantic textual similarity, transfer, and clustering tasks demonstrate the effectiveness of our proposed model compared to strong baselines. Code is available at \url{https://github.com/lemon0830/promptCSE}.",
}
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<abstract>Contrastive learning has become a new paradigm for unsupervised sentence embeddings.Previous studies focus on instance-wise contrastive learning, attempting to construct positive pairs with textual data augmentation. In this paper, we propose a novel Contrastive learning method with Prompt-derived Virtual semantic Prototypes (ConPVP). Specifically, with the help of prompts, we construct virtual semantic prototypes to each instance, and derive negative prototypes by using the negative form of the prompts. Using a prototypical contrastive loss, we enforce the anchor sentence embedding to be close to its corresponding semantic prototypes, and far apart from the negative prototypes as well as the prototypes of other sentences. Extensive experimental results on semantic textual similarity, transfer, and clustering tasks demonstrate the effectiveness of our proposed model compared to strong baselines. Code is available at https://github.com/lemon0830/promptCSE.</abstract>
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%0 Conference Proceedings
%T Contrastive Learning with Prompt-derived Virtual Semantic Prototypes for Unsupervised Sentence Embedding
%A Zeng, Jiali
%A Yin, Yongjing
%A Jiang, Yufan
%A Wu, Shuangzhi
%A Cao, Yunbo
%Y Goldberg, Yoav
%Y Kozareva, Zornitsa
%Y Zhang, Yue
%S Findings of the Association for Computational Linguistics: EMNLP 2022
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F zeng-etal-2022-contrastive
%X Contrastive learning has become a new paradigm for unsupervised sentence embeddings.Previous studies focus on instance-wise contrastive learning, attempting to construct positive pairs with textual data augmentation. In this paper, we propose a novel Contrastive learning method with Prompt-derived Virtual semantic Prototypes (ConPVP). Specifically, with the help of prompts, we construct virtual semantic prototypes to each instance, and derive negative prototypes by using the negative form of the prompts. Using a prototypical contrastive loss, we enforce the anchor sentence embedding to be close to its corresponding semantic prototypes, and far apart from the negative prototypes as well as the prototypes of other sentences. Extensive experimental results on semantic textual similarity, transfer, and clustering tasks demonstrate the effectiveness of our proposed model compared to strong baselines. Code is available at https://github.com/lemon0830/promptCSE.
%R 10.18653/v1/2022.findings-emnlp.522
%U https://aclanthology.org/2022.findings-emnlp.522
%U https://doi.org/10.18653/v1/2022.findings-emnlp.522
%P 7042-7053
Markdown (Informal)
[Contrastive Learning with Prompt-derived Virtual Semantic Prototypes for Unsupervised Sentence Embedding](https://aclanthology.org/2022.findings-emnlp.522) (Zeng et al., Findings 2022)
ACL