Training a parser for machine translation reordering

  • Authors:
  • Jason Katz-Brown;Slav Petrov;Ryan McDonald;Franz Och;David Talbot;Hiroshi Ichikawa;Masakazu Seno;Hideto Kazawa

  • Affiliations:
  • Google;Google;Google;Google;Google;Google;Google;Google

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

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Abstract

We propose a simple training regime that can improve the extrinsic performance of a parser, given only a corpus of sentences and a way to automatically evaluate the extrinsic quality of a candidate parse. We apply our method to train parsers that excel when used as part of a reordering component in a statistical machine translation system. We use a corpus of weakly-labeled reference reorderings to guide parser training. Our best parsers contribute significant improvements in subjective translation quality while their intrinsic attachment scores typically regress.