Automatic Learning of Parallel Dependency Treelet Pairs

  • Yuan Ding
  • Martha Palmer
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3248)


Induction of synchronous grammars from empirical data has long been an unsolved problem; despite generative synchronous grammars theoretically suit the machine translation task very well. This fact is mainly due to pervasive structural divergences between languages. This paper presents a statistical approach that learns dependency structure mappings from parallel corpora. The new algorithm automatically learns parallel dependency treelet pairs from loosely matched non-isomorphic dependency trees while keeping computational complexity polynomial in the length of the sentences. A set of heuristics is introduced and specifically optimized for parallel treelet learning purposes using Minimum Error Rate training.


Word Pair Machine Translation Content Word Heuristic Function Dependency Tree 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Yuan Ding
    • 1
  • Martha Palmer
    • 1
  1. 1.Department of Computer and Information ScienceUniversity of PennsylvaniaPhiladelphiaUSA

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