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

We describe LMU Munich's machine translation system for EnglishGerman translation which was used to participate in the WMT19 shared task on supervised news translation. We specifically participated in the documentlevel 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.