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Although contextualized embeddings generated from large-scale pre-trainedmodels perform well in many tasks, traditional static embeddings (e.g.,Skip-gram, Word2Vec) still play an important role in low-resource andlightweight settings due to their low computational cost, ease of deployment,and stability. In this paper, we aim to improve word embeddings by 1)incorporating more contextual information from existing pre-trained models intothe Skip-gram framework, which we call Context-to-Vec; 2) proposing apost-processing retrofitting method for static embeddings independent oftraining by employing priori synonym knowledge and weighted vectordistribution. Through extrinsic and intrinsic tasks, our methods are wellproven to outperform the baselines by a large margin.

Using Context-to-Vector with Graph Retrofitting to Improve Word Embeddings