Combining Local and Document-Level Context: The LMU Munich Neural Machine Translation System at WMT19

Dario Stojanovski, Alexander Fraser


Abstract
We describe LMU Munich’s machine translation system for English→German translation which was used to participate in the WMT19 shared task on supervised news translation. We specifically participated in the document-level MT track. The system used as a primary submission is a context-aware Transformer capable of both rich modeling of limited contextual information and integration of large-scale document-level context with a less rich representation. We train this model by fine-tuning a big Transformer baseline. Our experimental results show that document-level context provides for large improvements in translation quality, and adding a rich representation of the previous sentence provides a small additional gain.
Anthology ID:
W19-5345
Volume:
Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1)
Month:
August
Year:
2019
Address:
Florence, Italy
Editors:
Ondřej Bojar, Rajen Chatterjee, Christian Federmann, Mark Fishel, Yvette Graham, Barry Haddow, Matthias Huck, Antonio Jimeno Yepes, Philipp Koehn, André Martins, Christof Monz, Matteo Negri, Aurélie Névéol, Mariana Neves, Matt Post, Marco Turchi, Karin Verspoor
Venue:
WMT
SIG:
SIGMT
Publisher:
Association for Computational Linguistics
Note:
Pages:
400–406
Language:
URL:
https://aclanthology.org/W19-5345
DOI:
10.18653/v1/W19-5345
Bibkey:
Cite (ACL):
Dario Stojanovski and Alexander Fraser. 2019. Combining Local and Document-Level Context: The LMU Munich Neural Machine Translation System at WMT19. In Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1), pages 400–406, Florence, Italy. Association for Computational Linguistics.
Cite (Informal):
Combining Local and Document-Level Context: The LMU Munich Neural Machine Translation System at WMT19 (Stojanovski & Fraser, WMT 2019)
Copy Citation:
PDF:
https://aclanthology.org/W19-5345.pdf