@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}
}
