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Discriminative ridge regression algorithm for adaptation in statistical machine translation

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Abstract

We present a simple and reliable method for estimating the log-linear weights of a state-of-the-art machine translation system, which takes advantage of the method known as discriminative ridge regression (DRR). Since inappropriate weight estimations lead to a wide variability of translation quality results, reaching a reliable estimate for such weights is critical for machine translation research. For this reason, a variety of methods have been proposed to reach reasonable estimates. In this paper, we present an algorithmic description and empirical results proving that DRR is able to provide comparable translation quality when compared to state-of-the-art estimation methods [i.e. MERT (Och in Proceedings of the annual meeting of the association for computational linguistics, 2003) and MIRA (Cherry and Foster in Proceedings of the North American chapter of the association for computational linguistics, 2012)], with a reduction in computational cost. Moreover, the empirical results reported are coherent across different corpora and language pairs.

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  1. www.aclweb.org/anthology/P/P16/.

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Authors and Affiliations

  1. Pattern Recognition and Human Language Technology Research Center, Universitat Politècnica de València, Valencia, Spain

    Mara Chinea-Rios & Francisco Casacuberta

  2. Sciling, Valencia, Spain

    Germán Sanchis-Trilles

Authors
  1. Mara Chinea-Rios
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  2. Germán Sanchis-Trilles
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  3. Francisco Casacuberta
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Corresponding author

Correspondence to Mara Chinea-Rios.

Additional information

The research leading to these results were partially supported by projects CoMUN-HaT-TIN2015-70924-C2-1-R (MINECO/FEDER) and PROMETEO/2018/004. We also acknowledge NVIDIA for the donation of a GPU used in this work.

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Chinea-Rios, M., Sanchis-Trilles, G. & Casacuberta, F. Discriminative ridge regression algorithm for adaptation in statistical machine translation. Pattern Anal Applic 22, 1293–1305 (2019). https://doi.org/10.1007/s10044-018-0720-5

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