Using paraphrases for parameter tuning in statistical machine translation
Nitin Madnani, Necip Fazıl Ayan, Philip Resnik, Bonnie Jean Dorr · 2007
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.