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CMV-BERT: Contrastive multi-vocab pretraining of BERT

2020-12-29 14:23:50
Wei Zhu, Daniel Cheung
arXiv_CL
         
Abstract

In this work, we represent CMV-BERT, which improves the pretraining of a language model via two ingredients: (a) contrastive learning, which is well studied in the area of computer vision; (b) multiple vocabularies, one of which is fine-grained and the other is coarse-grained. The two methods both provide different views of an original sentence, and both are shown to be beneficial. Downstream tasks demonstrate our proposed CMV-BERT are effective in improving the pretrained language models.

Abstract (translated)

URL

https://arxiv.org/abs/2012.14763

PDF

https://arxiv.org/pdf/2012.14763.pdf

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