Using paraphrases for parameter tuning in statistical machine translation

Authors:
Nitin Madnani;Necip Fazil Ayan;Philip Resnik;Bonnie J. Dorr
Affiliations:
University of Maryland, College Park, MD;University of Maryland, College Park, MD;University of Maryland, College Park, MD;University of Maryland, College Park, MD
Venue:
StatMT '07 Proceedings of the Second Workshop on Statistical Machine Translation
Year:
2007

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Abstract

Most state-of-the-art statistical machine translation systems use log-linear models, which are defined in terms of hypothesis features and weights for those features. It is standard to tune the feature weights in order to maximize a translation quality metric, using held-out test sentences and their corresponding reference translations. However, obtaining reference translations is expensive. In this paper, we introduce a new full-sentence paraphrase technique, based on English-to-English decoding with an MT system, and we demonstrate that the resulting paraphrases can be used to drastically reduce the number of human reference translations needed for parameter tuning, without a significant decrease in translation quality.