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SemGloVe: Semantic Co-occurrences for GloVe from BERT

2020-12-30 15:38:26
Leilei Gan, Zhiyang Teng, Yue Zhang, Linchao Zhu, Fei Wu, Yi Yang

Abstract

GloVe learns word embeddings by leveraging statistical information from word co-occurrence matrices. However, word pairs in the matrices are extracted from a predefined local context window, which might lead to limited word pairs and potentially semantic irrelevant word pairs. In this paper, we propose SemGloVe, which distills semantic co-occurrences from BERT into static GloVe word embeddings. Particularly, we propose two models to extract co-occurrence statistics based on either the masked language model or the multi-head attention weights of BERT. Our methods can extract word pairs without limiting by the local window assumption and can define the co-occurrence weights by directly considering the semantic distance between word pairs. Experiments on several word similarity datasets and four external tasks show that SemGloVe can outperform GloVe.

Abstract (translated)

URL

https://arxiv.org/abs/2012.15197

PDF

https://arxiv.org/pdf/2012.15197.pdf


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