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WhiteningBERT: An Easy Unsupervised Sentence Embedding Approach

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arxiv 2104.01767 v3 pith:B5PKKP3K submitted 2021-04-05 cs.CL

WhiteningBERT: An Easy Unsupervised Sentence Embedding Approach

Junjie Huang , Duyu Tang , Wanjun Zhong , Shuai Lu , Linjun Shou , Ming Gong , Daxin Jiang , Nan Duan This is my paper
classification cs.CL
keywords sentenceunsupervisedbetterconducteasyembeddinglayersonly
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Producing the embedding of a sentence in an unsupervised way is valuable to natural language matching and retrieval problems in practice. In this work, we conduct a thorough examination of pretrained model based unsupervised sentence embeddings. We study on four pretrained models and conduct massive experiments on seven datasets regarding sentence semantics. We have there main findings. First, averaging all tokens is better than only using [CLS] vector. Second, combining both top andbottom layers is better than only using top layers. Lastly, an easy whitening-based vector normalization strategy with less than 10 lines of code consistently boosts the performance.

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Cited by 1 Pith paper

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  1. SimCSE: Simple Contrastive Learning of Sentence Embeddings

    cs.CL 2021-04 conditional novelty 8.0

    SimCSE achieves 76.3% unsupervised and 81.6% supervised Spearman's correlation on STS tasks with BERT-base, improving prior best results by 4.2% and 2.2% via simple contrastive learning.