Computer Science > Computation and Language
arXiv:2205.06603 (cs)
[Submitted on 13 May 2022]
Title:Improving Contextual Representation with Gloss Regularized Pre-training
View a PDF of the paper titled Improving Contextual Representation with Gloss Regularized Pre-training, by Yu Lin and 3 other authors
View PDFAbstract:Though achieving impressive results on many NLP tasks, the BERT-like masked language models (MLM) encounter the discrepancy between pre-training and inference. In light of this gap, we investigate the contextual representation of pre-training and inference from the perspective of word probability distribution. We discover that BERT risks neglecting the contextual word similarity in pre-training. To tackle this issue, we propose an auxiliary gloss regularizer module to BERT pre-training (GR-BERT), to enhance word semantic similarity. By predicting masked words and aligning contextual embeddings to corresponding glosses simultaneously, the word similarity can be explicitly modeled. We design two architectures for GR-BERT and evaluate our model in downstream tasks. Experimental results show that the gloss regularizer benefits BERT in word-level and sentence-level semantic representation. The GR-BERT achieves new state-of-the-art in lexical substitution task and greatly promotes BERT sentence representation in both unsupervised and supervised STS tasks.
| Comments: | Accepted to Findings of NAACL 2022 |
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG) |
| Cite as: | arXiv:2205.06603 [cs.CL] |
| (orarXiv:2205.06603v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2205.06603 arXiv-issued DOI via DataCite |
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View a PDF of the paper titled Improving Contextual Representation with Gloss Regularized Pre-training, by Yu Lin and 3 other authors
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