Using Alignment Templates to Infer Shallow-Transfer Machine Translation Rules

  • Felipe Sánchez-Martínez
  • Hermann Ney
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4139)


When building rule-based machine translation systems, a considerable human effort is needed to code the transfer rules that are able to translate source-language sentences into grammatically correct target-language sentences. In this paper we describe how to adapt the alignment templates used in statistical machine translation to the rule-based machine translation framework. The alignment templates are converted into structural transfer rules that are used by a shallow-transfer machine translation engine to produce grammatically correct translations. As the experimental results show there is a considerable improvement in the translation quality as compared to word-for-word translation (when no transfer rules are used), and the translation quality is close to that achieved when hand-coded transfer rules are used. The method presented is entirely unsupervised, and needs only a parallel corpus, two morphological analysers, and two part-of-speech taggers, such as those used by the machine translation system in which the inferred transfer rules are integrated.


Machine Translation Target Language Source Language Statistical Machine Translation Word Class 
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 2006

Authors and Affiliations

  • Felipe Sánchez-Martínez
    • 1
  • Hermann Ney
    • 1
  1. 1.Lehrstuhl für Informatik VI – Computer Science DepartmentRWTH Aachen UniversityAachenGermany

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