  This paper documents the Team Copenhagen system which placed first in the
CoNLL--SIGMORPHON 2018 shared task on universal morphological reinflection,
Task 2 with an overall accuracy of 49.87. Task 2 focuses on morphological
inflection in context: generating an inflected word form, given the lemma of
the word and the context it occurs in. Previous SIGMORPHON shared tasks have
focused on context-agnostic inflection---the "inflection in context" task was
introduced this year. We approach this with an encoder-decoder architecture
over character sequences with three core innovations, all contributing to an
improvement in performance: (1) a wide context window; (2) a multi-task
learning approach with the auxiliary task of MSD prediction; (3) training
models in a multilingual fashion.
