Contextual embeddings, such as ELMo and BERT, move beyond global word representations like Word2Vec and achieve ground-breaking performance on a wide range of natural language processing tasks. Contextual embeddings assign each word a representation based on its context, thereby capturing uses of words across varied contexts and encoding knowledge that transfers across languages. In this survey, we review existing contextual embedding models, cross-lingual polyglot pre-training, the application of contextual embeddings in downstream tasks, model compression, and model analyses.
@article{arxiv.2003.07278,
title = {A Survey on Contextual Embeddings},
author = {Qi Liu and Matt J. Kusner and Phil Blunsom},
journal= {arXiv preprint arXiv:2003.07278},
year = {2020}
}