Using POS information for statistical machine translation into morphologically rich languages

  • Authors:
  • Nicola Ueffing;Hermann Ney

  • Affiliations:
  • University of Technology;University of Technology

  • Venue:
  • EACL '03 Proceedings of the tenth conference on European chapter of the Association for Computational Linguistics - Volume 1
  • Year:
  • 2003

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Abstract

When translating from languages with hardly any inflectional morphology like English into morphologically rich languages, the English word forms often do not contain enough information for producing the correct fullform in the target language. We investigate methods for improving the quality of such translations by making use of part-of-speech information and maximum entropy modeling. Results for translations from English into Spanish and Catalan are presented on the LC-STAR corpus which consists of spontaneously spoken dialogues in the domain of appointment scheduling and travel planning.