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GloVe learns word embeddings by leveraging statistical information from wordco-occurrence matrices. However, word pairs in the matrices are extracted froma predefined local context window, which might lead to limited word pairs andpotentially semantic irrelevant word pairs. In this paper, we propose SemGloVe,which distills semantic co-occurrences from BERT into static GloVe wordembeddings. Particularly, we propose two models to extract co-occurrencestatistics based on either the masked language model or the multi-headattention weights of BERT. Our methods can extract word pairs without limitingby the local window assumption and can define the co-occurrence weights bydirectly considering the semantic distance between word pairs. Experiments onseveral word similarity datasets and four external tasks show that SemGloVe canoutperform GloVe.

SemGloVe: Semantic Co-occurrences for GloVe from BERT