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Purest ever example-based machine translation: Detailed presentation and assessment

  • Example-Based Mt
  • Published:
Machine Translation

Abstract

We have designed, implemented and assessed an EBMT system that can be dubbed the “purest ever built”: it strictly does not make any use of variables, templates or patterns, does not have any explicit transfer component, and does not require any preprocessing or training of the aligned examples. It uses only a specific operation, proportional analogy, that implicitly neutralizes divergences between languages and captures lexical and syntactic variations along the paradigmatic and syntagmatic axes without explicitly decomposing sentences into fragments. Exactly the same genuine implementation of such a core engine was evaluated on different tasks and language pairs. To begin with, we compared our system on two tasks of a previous MT evaluation campaign to rank it among other current state-of-the-art systems. Then, we illustrated the “universality” of our system by participating in a recent MT evaluation campaign, with exactly the same core engine, for a wide variety of language pairs. Finally, we studied the influence of extra data like dictionaries and paraphrases on the system performance.

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  1. Yves Lepage

    Present address: Université de Caen, campus 2, Bd Maréchal Juin, BP 5186, F-14032, Caen Cedex, France

Authors and Affiliations

  1. ATR Spoken Language Communication Research Labs, 619-0288, Keihanna, Kyōto, Japan

    Yves Lepage & Etienne Denoual

  2. GETA-CLIPS-IMAG, Université Joseph Fourier, 38041, Grenoble Cedex 9, France

    Etienne Denoual

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  1. Yves Lepage
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  2. Etienne Denoual
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Correspondence to Yves Lepage.

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Lepage, Y., Denoual, E. Purest ever example-based machine translation: Detailed presentation and assessment. Machine Translation 19, 251–282 (2005). https://doi.org/10.1007/s10590-006-9010-x

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