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arXiv · 1602.01925

Massively Multilingual Word Embeddings

Waleed Ammar·George Mulcaire·Yulia Tsvetkov·Guillaume Lample·Chris Dyer·Noah A. Smith
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Abstract

We introduce new methods for estimating and evaluating embeddings of words in more than fifty languages in a single shared embedding space. Our estimation methods, multiCluster and multiCCA, use dictionaries and monolingual data; they do not require parallel data. Our new evaluation method, multiQVEC-CCA, is shown to correlate better than previous ones with two downstream tasks (text categorization and parsing). We also describe a web portal for evaluation that will facilitate further research in this area, along with open-source releases of all our methods.

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Waleed Ammar, George Mulcaire, Yulia Tsvetkov, Guillaume Lample, Chris Dyer, Noah A. Smith. 2016-02-05. Massively Multilingual Word Embeddings. https://arxiv.org/abs/1602.01925

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