The RWTH Aachen University Machine Translation Systems for WMT 2019

no code implementations • WS 2019 • Jan Rosendahl, Christian Herold, Yunsu Kim, Miguel Gra{\c{c}}a, Weiyue Wang, Parnia Bahar, Yingbo Gao, Hermann Ney

For the De-En task, none of the tested methods gave a significant improvement over last years winning system and we end up with the same performance, resulting in 39. 6{\%} BLEU on newstest2019.

Attribute Language Modelling +3

The RWTH Aachen University Filtering System for the WMT 2018 Parallel Corpus Filtering Task

no code implementations • WS 2018 • Nick Rossenbach, Jan Rosendahl, Yunsu Kim, Miguel Gra{\c{c}}a, Aman Gokrani, Hermann Ney

We use several rule-based, heuristic methods to preselect sentence pairs.

Machine Translation Sentence +1

The RWTH Aachen University English-German and German-English Unsupervised Neural Machine Translation Systems for WMT 2018

no code implementations • WS 2018 • Miguel Gra{\c{c}}a, Yunsu Kim, Julian Schamper, Jiahui Geng, Hermann Ney

This paper describes the unsupervised neural machine translation (NMT) systems of the RWTH Aachen University developed for the English ↔ German news translation task of the \textit{EMNLP 2018 Third Conference on Machine Translation} (WMT 2018).

Machine Translation NMT +2

The RWTH Aachen University English-German and German-English Machine Translation System for WMT 2017

no code implementations • WS 2017 • Jan-Thorsten Peter, Andreas Guta, Tamer Alkhouli, Parnia Bahar, Jan Rosendahl, Nick Rossenbach, Miguel Gra{\c{c}}a, Hermann Ney

Machine Translation Translation

Extended Translation Models in Phrase-based Decoding

no code implementations • WS 2015 • Andreas Guta, Joern Wuebker, Miguel Gra{\c{c}}a, Yunsu Kim, Hermann Ney

Language Modelling Machine Translation +1