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Transducer-learning experiments on language understanding

  • David Picó
  • Enrique Vidal
Conference paper
  • 83 Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1433)

Abstract

The interest in using Finite-State Models in a large variety of applications is recently growing as more powerful techniques for learning them from examples have been developed. Language Understanding can be approached this way as a problem of language translation in which the target language is a formal language rather than a natural one. Finite-state transducers are used to model the translation process, and are automatically learned from training data consisting of pairs of natural-language/formal-language sentences. The need for training data is dramatically reduced by performing a two-level learning process based on lexical/phrase categorization. Successful experiments are presented on a task consisting in the “understanding” of Spanish natural-language sentences describing dates and times, where the target formal language is the one used in the popular Unix command “at”.

Keywords

Machine Translation Language Translation Training Corpus Training Pair Input Sentence 
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 1998

Authors and Affiliations

  • David Picó
    • 1
  • Enrique Vidal
    • 1
  1. 1.Institut TecnolÒgic d'InformàticaUniversitat Politècnica de ValènciaValènciaSpain

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