The RWTH aachen machine translation system for WMT 2013

This work describes the statistical machine translation (SMT) systems of RWTH Aachen University developed for the evaluation campaign International Workshop on Spoken Language Translation (IWSLT) 2013. We participated in the English→French, English↔German, Arabic→English, Chinese→English and Slovenian↔English MT tracks and the English→French and English→German SLT tracks. We apply phrase-based and hierarchical SMT decoders, which are augmented by state-of-the-art extensions. The novel techniques we experimentally evaluate include discrimina-tive phrase training, a continuous space language model, a hierarchical reordering model, a word class language model, domain adaptation via data selection and system combina-tion of standard and reverse order models. By application of these methods we can show considerable improvements over the respective baseline systems. 1