Learning to Compute Word Embeddings On the Fly

arXiv: Learning, Volume abs/1706.00286, 2018.

Cited by: 43|Bibtex|Views237
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Other Links: dblp.uni-trier.de|academic.microsoft.com|arxiv.org

Abstract:

Words in natural language follow a Zipfian distribution whereby some words are frequent but most are rare. Learning representations for words in the ``long tailu0027u0027 of this distribution requires enormous amounts of data. Representations of rare words trained directly on end tasks are usually poor, requiring us to pre-train embedding...More

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