Vector Space Model for Adaptation in Statistical Machine Translation

@inproceedings{Chen2013VectorSM,
  title={Vector Space Model for Adaptation in Statistical Machine Translation},
  author={Boxing Chen and Roland Kuhn and George F. Foster},
  booktitle={ACL},
  year={2013}
}
This paper proposes a new approach to domain adaptation in statistical machine translation (SMT) based on a vector space model (VSM). The general idea is first to create a vector profile for the in-domain development (“dev”) set. This profile might, for instance, be a vector with a dimensionality equal to the number of training subcorpora; each entry in the vector reflects the contribution of a particular subcorpus to all the phrase pairs that can be extracted from the dev set. Then, for each… CONTINUE READING
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Key Quantitative Results

  • Experiments on large scale NIST evaluation data show improvements over strong baselines: +1.8 BLEU on Arabic to English and +1.4 BLEU on Chinese to English over a non-adapted baseline, and significant improvements in most circumstances over baselines with linear mixture model adaptation.

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