Abstract:BERT, the most popular deep learning language model, has yielded breakthrough results in various NLP tasks. However, the semantic representation space learned by BERT has the property of anisotropy. Therefore, BERT needs to be fine-tuned for certain downstream tasks such as Semantic Textual Similarity (STS). To overcome this problem and improve the sentence representation space, some contrastive learning methods have been proposed for fine-tuning BERT. However, existing contrastive learning models do not consi… Show more
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