The RWTH Aachen German to English MT System for IWSLT 2015
Jan-Thorsten Peter, Farzad Toutounchi, Stephan Peitz, Parnia Bahar, Andreas Guta, Hermann Ney · RWTH Publications (RWTH Aachen) · 2015
This work describes the statistical machine translation (SMT) systems of RWTH Aachen University developed for the evaluation campaign of the International Workshop on Spoken Language Translation (IWSLT) 2015. We participated in the MT and SLT tracks for the German!English language pair. We employ our state-of-the-art phrase-based and hierarchical phrase-based baseline systems for the MT track. The phrase-based system is augmented with joint translation and reordering model and maximum expected BLEU training for phrasal, lexical and reordering models. Furthermore, we apply feed-forward and recurrent neural language and translation models for reranking. We also train attention-based neural network models and utilize them in reranking the n-best lists for both phrase-based and hierarchical setups. On top of all our systems, we use system combination to enhance the translation quality by combining individually trained systems. In the SLT track, we additionally perform punctuation prediction on the automatic transcriptions employing hierarchical phrase-based translation.