Mining Parallel Resources for Machine Translation from Comparable Corpora

  • Santanu Pal
  • Partha Pakray
  • Alexander Gelbukh
  • Josef van Genabith
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9041)

Abstract

Good performance of Statistical Machine Translation (SMT) is usually achieved with huge parallel bilingual training corpora, because the translations of words or phrases are computed basing on bilingual data. However, in case of low-resource language pairs such as English-Bengali, the performance is affected by insufficient amount of bilingual training data. Recently, comparable corpora became widely considered as valuable resources for machine translation. Though very few cases of sub-sentential level parallelism are found between two comparable documents, there are still potential parallel phrases in comparable corpora. Mining parallel data from comparable corpora is a promising approach to collect more parallel training data for SMT. In this paper, we propose an automatic alignment of English-Bengali comparable sentences from comparable documents. We use a novel textual entailment method and distributional semantics for text similarity. Subsequently, we apply template-based phrase extraction technique to aligned parallel phrases from comparable sentence pairs. The effectiveness of our approach is demonstrated by using parallel phrases as additional training examples for an English-Bengali phrase-based SMT system. Our system achieves significant improvement in terms of translation quality over the baseline system.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Santanu Pal
    • 1
  • Partha Pakray
    • 2
  • Alexander Gelbukh
    • 3
  • Josef van Genabith
    • 1
  1. 1.Universität Des SaarlandesSaarbruckenGermany
  2. 2.National Institute of TechnologyMizoramIndia
  3. 3.Centro de Investigación en ComputaciónInstituto Politécnico NacionalMexico CityMexico

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