A Survey on Contextual Embeddings

• URL: http://arxiv.org/abs/2003.07278v2
• Date: Mon, 13 Apr 2020 10:49:17 GMT
• Title: A Survey on Contextual Embeddings
• Authors: Qi Liu, Matt J. Kusner, Phil Blunsom
• Abstract summary: Contextual embeddings assign each word a representation based on its context, capturing uses of words across varied contexts and encoding knowledge that transfers across languages. We review existing contextual embedding models, cross-lingual polyglot pre-training, the application of contextual embeddings in downstream tasks, model compression, and model analyses.
• Score: 48.04732268018772
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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