Unsupervised structure prediction with non-parallel multilingual guidance

  • Authors:
  • Shay B. Cohen;Dipanjan Das;Noah A. Smith

  • Affiliations:
  • Carnegie Mellon University, Pittsburgh, PA;Carnegie Mellon University, Pittsburgh, PA;Carnegie Mellon University, Pittsburgh, PA

  • Venue:
  • EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
  • Year:
  • 2011

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Abstract

We describe a method for prediction of linguistic structure in a language for which only unlabeled data is available, using annotated data from a set of one or more helper languages. Our approach is based on a model that locally mixes between supervised models from the helper languages. Parallel data is not used, allowing the technique to be applied even in domains where human-translated texts are unavailable. We obtain state-of-the-art performance for two tasks of structure prediction: unsupervised part-of-speech tagging and unsupervised dependency parsing.