Using Context-to-Vector with Graph Retrofitting to Improve Word Embeddings. (arXiv:2210.16848v1 [cs.CL])

Although contextualized embeddings generated from large-scale pre-trained
models perform well in many tasks, traditional static embeddings (e.g.,
Skip-gram, Word2Vec) still play an important role in low-resource and
lightweight 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 into
the Skip-gram framework, which we call Context-to-Vec; 2) proposing a
post-processing retrofitting method for static embeddings independent of
training by …

arxiv context graph vector word embeddings