Inducing Crosslingual Distributed Representations of Words

Alexandre Klementiev, Ivan Titov, Binod Bhattarai

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Distributed representations of words have proven extremely useful in numerous natural language processing tasks. Their appeal is that they can help alleviate data sparsity problems common to supervised learning. Methods for inducing these representations require only unlabeled language data, which are plentiful for many natural languages. In this work, we induce distributed representations for a pair of languages jointly. We treat it as a multitask learning problem where each task corresponds to a single word, and task relatedness is derived from co-occurrence statistics in bilingual parallel data. These representations can be used for a number of crosslingual learning tasks, where a learner can be trained on annotations present in one language and applied to test data in another. We show that our representations are informative by using them for crosslingual document classification, where classifiers trained on these representations substantially outperform strong baselines (e.g. machine translation) when applied to a new language.
Original languageEnglish
Title of host publicationCOLING 2012, 24th International Conference on Computational Linguistics, Proceedings of the Conference: Technical Papers, 8-15 December 2012, Mumbai, India
PublisherAssociation for Computational Linguistics
Pages1459-1474
Number of pages16
Publication statusPublished - Dec 2012

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