  Document-level context for neural machine translation (NMT) is crucial to
improve the translation consistency and cohesion, the translation of ambiguous
inputs, as well as several other linguistic phenomena. Many works have been
published on the topic of document-level NMT, but most restrict the system to
only local context, typically including just the one or two preceding sentences
as additional information. This might be enough to resolve some ambiguous
inputs, but it is probably not sufficient to capture some document-level
information like the topic or style of a conversation. When increasing the
context size beyond just the local context, there are two challenges: (i)
the~memory usage increases exponentially (ii) the translation performance
starts to degrade. We argue that the widely-used attention mechanism is
responsible for both issues. Therefore, we propose a constrained attention
variant that focuses the attention on the most relevant parts of the sequence,
while simultaneously reducing the memory consumption. For evaluation, we
utilize targeted test sets in combination with novel evaluation techniques to
analyze the translations in regards to specific discourse-related phenomena. We
find that our approach is a good compromise between sentence-level NMT vs
attending to the full context, especially in low resource scenarios.
