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arXiv · 1604.00425

Cross-lingual Models of Word Embeddings: An Empirical Comparison

Shyam Upadhyay·Manaal Faruqui·Chris Dyer·Dan Roth
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Abstract

Despite interest in using cross-lingual knowledge to learn word embeddings for various tasks, a systematic comparison of the possible approaches is lacking in the literature. We perform an extensive evaluation of four popular approaches of inducing cross-lingual embeddings, each requiring a different form of supervision, on four typographically different language pairs. Our evaluation setup spans four different tasks, including intrinsic evaluation on mono-lingual and cross-lingual similarity, and extrinsic evaluation on downstream semantic and syntactic applications. We show that models which require expensive cross-lingual knowledge almost always perform better, but cheaply supervised models often prove competitive on certain tasks.

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Shyam Upadhyay, Manaal Faruqui, Chris Dyer, Dan Roth. 2016-06-08. Cross-lingual Models of Word Embeddings: An Empirical Comparison. https://arxiv.org/abs/1604.00425

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