Google Research

Training a Parser for Machine Translation Reordering

  • Jason Katz-Brown
  • Slav Petrov
  • Ryan McDonald
  • Franz Och
  • David Talbot
  • Hiroshi Ichikawa
  • Masakazu Seno
Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing (EMNLP '11)


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.

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