SemGloVe: Semantic Co-Occurrences for GloVe From BERT
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 <i>SemGloVe</i>, which distills <i>semantic co-occurrences</i> 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 limited 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 external tasks show that SemGloVe can outperform GloVe.