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Hierarchical Finite-State Models for Speech Translation Using Categorization of Phrases

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Part of the Lecture Notes in Computer Science book series (LNTCS,volume 6008)

Abstract

In this work a hierarchical translation model is formally defined and integrated in a speech translation system. As it is well known, the relations between two languages are better arranged in terms of phrases than in terms of running words. Nevertheless phrase-based models may suffer from data sparsity at training time. The aim of this work is to improve current speech translation systems by integrating categorization within the translation model. The categories are sets of phrases either linguistically or statistically motivated. Both category and translation and acoustic models are within the framework of finite-state models. In what temporal cost is concerned, finite-state models count on efficient decoding algorithms. Regarding the spatial cost, all the models where integrated on-the-fly at decoding time, allowing an efficient use of memory.

Keywords

  • Machine Translation
  • Target Language
  • Acoustic Model
  • Source Language
  • Translation Model

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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Justo, R., Pérez, A., Torres, M.I., Casacuberta, F. (2010). Hierarchical Finite-State Models for Speech Translation Using Categorization of Phrases. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2010. Lecture Notes in Computer Science, vol 6008. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12116-6_41

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  • DOI: https://doi.org/10.1007/978-3-642-12116-6_41

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-12115-9

  • Online ISBN: 978-3-642-12116-6

  • eBook Packages: Computer ScienceComputer Science (R0)