The University of Maryland Statistical Machine Translation System for the Fourth Workshop on Machine Translation

نویسندگان

  • Chris Dyer
  • Hendra Setiawan
  • Yuval Marton
  • Philip Resnik
چکیده

This paper describes the techniques we explored to improve the translation of news text in the German-English and Hungarian-English tracks of the WMT09 shared translation task. Beginning with a convention hierarchical phrase-based system, we found benefits for using word segmentation lattices as input, explicit generation of beginning and end of sentence markers, minimum Bayes risk decoding, and incorporation of a feature scoring the alignment of function words in the hypothesized translation. We also explored the use of monolingual paraphrases to improve coverage, as well as co-training to improve the quality of the segmentation lattices used, but these did not lead to improvements.

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تاریخ انتشار 2009