The RWTH Aachen University English-Romanian Machine Translation System for WMT 2016
Jan-Thorsten Peter, Tamer Alkhouli, Andreas Guta, Hermann Ney · 2016
This paper describes the statistical machine translation system developed at RWTH Aachen University for the English!Romanian translation task of the ACL 2016 First Conference on Machine Translation (WMT 2016). We combined three different state-ofthe-art systems in a system combination: A phrase-based system, a hierarchical phrase-based system and an attentionbased neural machine translation system. The phrase-based and the hierarchical phrase-based systems make use of a language model trained on all available data, a language model trained on the bilingual data and a word class language model. In addition, we utilized a recurrent neural network language model and a bidirectional recurrent neural network translation model for reranking the output of both systems. The attention-based neural machine translation system was trained using all bilingual data together with the backtranslated data from the News Crawl 2015