Two-step Data Selection and RBMT-Style Synthetic Rules

This paper presents the machine trans-lation systems submitted by the Abu-MaTran project to the WMT 2014 trans-lation task. The language pair concerned is English–French with a focus on French as the target language. The French to En-glish translation direction is also consid-ered, based on the word alignment com-puted in the other direction. Large lan-guage and translation models are built us-ing all the datasets provided by the shared task organisers, as well as the monolin-gual data from LDC. To build the trans-lation models, we apply a two-step data selection method based on bilingual cross-entropy difference and vocabulary satura-tion, considering each parallel corpus in-dividually. Synthetic translation rules are extracted from the development sets and used to train another translation model. We then interpolate the translation mod-els, minimising the perplexity on the de-velopment sets, to obtain our final SMT system. Our submission for the English to French translation task was ranked second amongst nine teams and a total of twenty submissions