The RWTH Aachen Machine Translation System for IWSLT 2016

Jan-Thorsten Peter, Andreas Guta, Nick Rossenbach, Miguel Graça, Hermann Ney


Abstract
This work describes the statistical machine translation (SMT) systems of RWTH Aachen University developed for the evaluation campaign of International Workshop on Spoken Language Translation (IWSLT) 2016. We have participated in the MT track for the German→English language pair employing our state-of-the-art phrase-based system, neural machine translation implementation and our joint translation and reordering decoder. Furthermore, we have applied feed-forward and recurrent neural language and translation models for reranking. The attention-based approach has been used for reranking the n-best lists for both phrasebased and hierarchical setups. On top of these systems, we make use of system combination to enhance the translation quality by combining individually trained systems.
Anthology ID:
2016.iwslt-1.22
Volume:
Proceedings of the 13th International Conference on Spoken Language Translation
Month:
December 8-9
Year:
2016
Address:
Seattle, Washington D.C
Editors:
Mauro Cettolo, Jan Niehues, Sebastian Stüker, Luisa Bentivogli, Rolando Cattoni, Marcello Federico
Venue:
IWSLT
SIG:
SIGSLT
Publisher:
International Workshop on Spoken Language Translation
Note:
Pages:
Language:
URL:
https://aclanthology.org/2016.iwslt-1.22
DOI:
Bibkey:
Cite (ACL):
Jan-Thorsten Peter, Andreas Guta, Nick Rossenbach, Miguel Graça, and Hermann Ney. 2016. The RWTH Aachen Machine Translation System for IWSLT 2016. In Proceedings of the 13th International Conference on Spoken Language Translation, Seattle, Washington D.C. International Workshop on Spoken Language Translation.
Cite (Informal):
The RWTH Aachen Machine Translation System for IWSLT 2016 (Peter et al., IWSLT 2016)
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PDF:
https://aclanthology.org/2016.iwslt-1.22.pdf