@conference {13889, title = {Using paraphrases for parameter tuning in statistical machine translation}, booktitle = {Proceedings of the Second Workshop on Statistical Machine Translation}, series = {StatMT {\textquoteright}07}, year = {2007}, month = {2007///}, pages = {120 - 127}, publisher = {Association for Computational Linguistics}, organization = {Association for Computational Linguistics}, address = {Stroudsburg, PA, USA}, 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.}, url = {http://dl.acm.org/citation.cfm?id=1626355.1626371}, author = {Madnani,Nitin and Ayan,Necip Fazil and Resnik, Philip and Dorr, Bonnie J} }