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Learning Bilingual Word Representations by Marginalizing Alignments
We present a probabilistic model that simultaneously learns alignments and
distributed representations for bilingual data. By marginalizing over word
alignments the model captures a larger semantic context than prior work relying
on hard alignments. The advantage of this approach is demonstrated in a
cross-lingual classification task, where we outperform the prior published
state of the art.Comment: Proceedings of ACL 2014 (Short Papers
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