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International Colloquium on Grammatical Inference

ICGI 2004: Grammatical Inference: Algorithms and Applications pp 3–15Cite as

Learning Finite-State Models for Machine Translation

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3264))

Abstract

In formal language theory finite-state transducers are well-know models for “input-output” rational mappings between two languages. Even if more powerful, recursive models can be used to account for more complex mappings, it has been argued that the input-output relations underlying most usual natural language pairs are essentially rational. Moreover, the relative simplicity of these mappings has recently lead to the development of techniques for learning finite-state transducers from a training set of input-output sentence pairs of the languages considered. Following these arguments, in the last few years a number of machine translation systems have been developed based on stochastic finite-state transducers. Here we review the statistical statement of Machine Translation and how the corresponding modelling, learning and search problems can be solved by using stochastic finite-state transducers. We also review the results achieved by the systems developed under this paradigm. After presenting the traditional approach, where transducer learning is mainly solved under the grammatical inference framework, we propose a new approach where learning is explicitly considered as a statistical estimation problem and the whole stochastic finite-state transducer learning problem is solved by expectation maximisation.

This work was partially supported by the European Union project TT2 (IST-2001-32091) and by the Spanish project TEFATE (TIC 2003-08681-C02-02).

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Author information

Authors and Affiliations

  1. Departamento de Sistemas Informáticos y Computación, Instituto Tecnológico de Informática, Universidad Politécnica de Valencia, 46071, Valencia, Spain

    Enrique Vidal & Francisco Casacuberta

Authors
  1. Enrique Vidal
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  2. Francisco Casacuberta
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Editor information

Editors and Affiliations

  1. Institute of Informatics and Telecommunications, National Centre for Scientific Research “Demokritos”, Athens, Greece

    Georgios Paliouras

  2. Department of Biosciences and Informatics, Keio University, 3-14-1 Hiyoshi, Kohoku-ku, 223-8522, Yokohama, Japan

    Yasubumi Sakakibara

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Vidal, E., Casacuberta, F. (2004). Learning Finite-State Models for Machine Translation. In: Paliouras, G., Sakakibara, Y. (eds) Grammatical Inference: Algorithms and Applications. ICGI 2004. Lecture Notes in Computer Science(), vol 3264. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30195-0_2

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  • DOI: https://doi.org/10.1007/978-3-540-30195-0_2

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-23410-4

  • Online ISBN: 978-3-540-30195-0

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