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Grammatical Inference for Syntax-Based Statistical Machine Translation

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Grammatical Inference: Algorithms and Applications (ICGI 2006)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 4201))

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Abstract

In this article we present a syntax-based translation system, called TABL (Translation using Alignment-Based Learning). It translates natural language sentences by mapping grammar rules (which are induced by the Alignment-Based Learning grammatical inference framework) of the source language to those of the target language. By parsing a sentence in the source language, the grammar rules in the derivation are translated using the mapping and subsequently, a derivation in the target language is generated. The initial results are encouraging, illustrating that this is a valid machine translation approach.

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References

  1. Brown, P.F., Della Pietra, S.A., Della Pietra, V.J., Mercer, R.L.: The mathematics of statistical machine translation: Parameter estimation. Computational Linguistics 19, 263–312 (1993)

    Google Scholar 

  2. Dan Melamed, I.: Statistical machine translation by parsing. In: 42th Annual Meeting of the Association for Computational Linguistics, Barcelona, Spain (2004)

    Google Scholar 

  3. Probst, K.: Automatically Induced Syntactic Transfer Rules for Machine Translation under a Very Limited Data Scenario. PhD thesis, Carnegie Mellon University, Pittsburgh, PA, USA (2005)

    Google Scholar 

  4. van Zaanen, M.: Bootstrapping Structure into Language: Alignment-Based Learning. PhD thesis, University of Leeds, Leeds, UK (January 2002)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Division of Information and Communication Sciences, Department of Computing, Macquarie University, 2109, Sydney, NSW, Australia

    Menno van Zaanen

  2. Language and Information Science, Tilburg University, Tilburg, The Netherlands

    Jeroen Geertzen

Authors
  1. Menno van Zaanen
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  2. Jeroen Geertzen
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Editor information

Editors and Affiliations

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

    Yasubumi Sakakibara

  2. Dept. of Computer Science, Kyoto Sangyo University, Kamigamo Motoyama, Kita-ku, Kyoto, Japan

    Satoshi Kobayashi

  3. Japan Biological Informatics Consortium, 10F TIME24 Building, 2-45 Aomi, Koto-ku, 135-8073, Tokyo, Japan

    Kengo Sato

  4. Department of Information and Communication Engineering, Graduate School of Electro-Communications, The University of Electro-Communications, 1-5-1 Chofugaoka, Chofu-shi, 182-8585, Tokyo, Japan

    Tetsuro Nishino

  5. Department of Information and Communication Engineering, Faculty of Electro-Communications, The University of Electro-Communications, Chofugaoka 1–5–1, Chofu, 182-8585, Tokyo, Japan

    Etsuji Tomita

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© 2006 Springer-Verlag Berlin Heidelberg

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van Zaanen, M., Geertzen, J. (2006). Grammatical Inference for Syntax-Based Statistical Machine Translation. In: Sakakibara, Y., Kobayashi, S., Sato, K., Nishino, T., Tomita, E. (eds) Grammatical Inference: Algorithms and Applications. ICGI 2006. Lecture Notes in Computer Science(), vol 4201. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11872436_35

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  • DOI: https://doi.org/10.1007/11872436_35

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-45264-5

  • Online ISBN: 978-3-540-45265-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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