Bayesian learning of phrasal tree-to-string templates

  • Authors:
  • Ding Liu;Daniel Gildea

  • Affiliations:
  • University of Rochester, Rochester, NY;University of Rochester, Rochester, NY

  • Venue:
  • EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 3 - Volume 3
  • Year:
  • 2009

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Abstract

We examine the problem of overcoming noisy word-level alignments when learning tree-to-string translation rules. Our approach introduces new rules, and re-estimates rule probabilities using EM. The major obstacles to this approach are the very reasons that word-alignments are used for rule extraction: the huge space of possible rules, as well as controlling overfitting. By carefully controlling which portions of the original alignments are reanalyzed, and by using Bayesian inference during re-analysis, we show significant improvement over the baseline rules extracted from word-level alignments.