Deep Continuous Prompt for Contrastive Learning of Sentence Embeddings

• URL: http://arxiv.org/abs/2203.06875v1
• Date: Mon, 14 Mar 2022 06:07:44 GMT
• Title: Deep Continuous Prompt for Contrastive Learning of Sentence Embeddings
• Authors: Yuxin Jiang and Wei Wang
• Abstract summary: We present a novel method which freezes the whole language model and only optimize the prefix deep continuous prompts. It not only tunes around 0.1% parameters of the original language model, but avoids the cumbersome computation of searching handcrafted prompts. Our proposed DCPCSE outperforms the state-of-the-art method SimCSE by a large margin.
• Score: 8.70715711885114
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The performance of sentence representation has been remarkably improved by the framework of contrastive learning. However, recent works still require full fine-tuning, which is quite inefficient for large-scaled pre-trained language models. To this end, we present a novel method which freezes the whole language model and only optimizes the prefix deep continuous prompts. It not only tunes around 0.1% parameters of the original language model, but avoids the cumbersome computation of searching handcrafted prompts. Experimental results show that our proposed DCPCSE outperforms the state-of-the-art method SimCSE by a large margin. We raise the performance of unsupervised BERT$_{base}$ and supervised RoBERTa$_{large}$ by 2.24 and 1.00 points, respectively. Our code is publicly avaliable at https://github.com/YJiangcm/DCPCSE

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