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Chunk-lattices for verb reordering in Arabic–English statistical machine translation

Special issues on machine translation for Arabic

  • Published:
Machine Translation

Abstract

Syntactic disfluencies in Arabic-to-English phrase-based SMT output are often due to incorrect verb reordering in Verb–Subject–Object sentences. As a solution, we propose a chunk-based reordering technique to automatically displace clause-initial verbs in the Arabic side of a word-aligned parallel corpus. This method is used to preprocess the training data, and to collect statistics about verb movements. From this analysis we build specific verb reordering lattices on the test sentences before decoding, and test different lattice-weighting schemes. Finally, we train a feature-rich discriminative model to predict likely verb reorderings for a given Arabic sentence. The model scores are used to prune the reordering lattice, leading to better word reordering at decoding time. The application of our reordering methods to the training and test data results in consistent improvements on the NIST-MT 2009 Arabic–English benchmark, both in terms of BLEU (+1.06%) and of reordering quality (+0.85%) measured with the Kendall Reordering Score.

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Author notes

  1. Daniele Pighin

    Present address: TALP Research Center-Universitat Politécnica de Catalunya, Barcelona, Spain

Authors and Affiliations

  1. Fondazione Bruno Kessler-IRST, Via Sommarive 18, Povo, Trento, Italy

    Arianna Bisazza, Daniele Pighin & Marcello Federico

Authors
  1. Arianna Bisazza
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  2. Daniele Pighin
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  3. Marcello Federico
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Correspondence to Arianna Bisazza.

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Bisazza, A., Pighin, D. & Federico, M. Chunk-lattices for verb reordering in Arabic–English statistical machine translation. Machine Translation 26, 85–103 (2012). https://doi.org/10.1007/s10590-011-9104-y

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  • DOI: https://doi.org/10.1007/s10590-011-9104-y

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