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Interactive Machine Translation

  • Alejandro Héctor Toselli
  • Enrique Vidal
  • Francisco Casacuberta

Abstract

Achieving high-quality translation between any pair of languages is not possible with the current Machine-Translation (MT) technology a human post-editing of the outputs of the MT system being necessary. Therefore, MT is a suitable area to apply the Interactive Pattern Recognition (IPR) framework and this application has led to what nowadays is known as Interactive Machine Translation (IMT). IMT can predict the translation of a given source sentence, and the human translator can accept or correct some of the errors. The text amended by the human translator can be used by the system to suggest new improved translations with the same translation models in an iterative process until the whole output is accepted by the human.

As in other areas where IPR is being applied, IMT offers a nice framework for adaptive learning. The consolidated translations obtained through the successive steps of the interaction process can easily be converted into new, fresh, training data, useful for dynamically adapting the system to the changing environment. On the other hand, IMT also allows one to take advantage of some available multi-modal interfaces to increase of productivity. Multi-modal interfaces and adaptive learning in IMT will be covered in Chaps.  7 and  8, respectively.

Keywords

European Union Machine Translation User Effort Target Sentence Confidence Measure 
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 London Limited 2011

Authors and Affiliations

  • Alejandro Héctor Toselli
    • Enrique Vidal
      • Francisco Casacuberta

        There are no affiliations available

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