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

Dario Stojanovski, Alexander Fraser

Fourth Conference on Machine Translation 2019

We describe LMU Munich's machine translation system for English to
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.