Cutting the long tail: hybrid language models for translation style adaptation

  • Authors:
  • Arianna Bisazza;Marcello Federico

  • Affiliations:
  • Fondazione Bruno Kessler Trento, Italy;Fondazione Bruno Kessler Trento, Italy

  • Venue:
  • EACL '12 Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics
  • Year:
  • 2012

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Abstract

In this paper, we address statistical machine translation of public conference talks. Modeling the style of this genre can be very challenging given the shortage of available in-domain training data. We investigate the use of a hybrid LM, where infrequent words are mapped into classes. Hybrid LMs are used to complement word-based LMs with statistics about the language style of the talks. Extensive experiments comparing different settings of the hybrid LM are reported on publicly available benchmarks based on TED talks, from Arabic to English and from English to French. The proposed models show to better exploit in-domain data than conventional word-based LMs for the target language modeling component of a phrase-based statistical machine translation system.