@inproceedings{stojanovski-fraser-2019-combining,
title = "Combining Local and Document-Level Context: The {LMU} {M}unich Neural Machine Translation System at {WMT}19",
author = "Stojanovski, Dario and
Fraser, Alexander",
booktitle = "Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1)",
month = aug,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W19-5345",
doi = "10.18653/v1/W19-5345",
pages = "400--406",
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.",
}
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%0 Conference Proceedings
%T Combining Local and Document-Level Context: The LMU Munich Neural Machine Translation System at WMT19
%A Stojanovski, Dario
%A Fraser, Alexander
%S Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1)
%D 2019
%8 aug
%I Association for Computational Linguistics
%C Florence, Italy
%F stojanovski-fraser-2019-combining
%X 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.
%R 10.18653/v1/W19-5345
%U https://aclanthology.org/W19-5345
%U https://doi.org/10.18653/v1/W19-5345
%P 400-406
Markdown (Informal)
[Combining Local and Document-Level Context: The LMU Munich Neural Machine Translation System at WMT19](https://aclanthology.org/W19-5345) (Stojanovski & Fraser, 2019)
ACL