A Survey on Contextual Embeddings

Qi Liu; Matt J. Kusner; Phil Blunsom

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arXiv · 2003.07278

A Survey on Contextual Embeddings

Qi Liu·Matt J. Kusner·Phil Blunsom

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Abstract

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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Qi Liu, Matt J. Kusner, Phil Blunsom. 2020-03-16. A Survey on Contextual Embeddings. https://arxiv.org/abs/2003.07278

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