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A Survey On Contextual Embeddings

Qi Liu, Matt J. Kusner, Phil Blunsom . Arxiv 2020 – 113 citations

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Efficiency Model Architecture Survey Paper Training Techniques

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.

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