  This paper presents the submissions by the University of Zurich to the
SIGMORPHON 2017 shared task on morphological reinflection. The task is to
predict the inflected form given a lemma and a set of morpho-syntactic
features. We focus on neural network approaches that can tackle the task in a
limited-resource setting. As the transduction of the lemma into the inflected
form is dominated by copying over lemma characters, we propose two recurrent
neural network architectures with hard monotonic attention that are strong at
copying and, yet, substantially different in how they achieve this. The first
approach is an encoder-decoder model with a copy mechanism. The second approach
is a neural state-transition system over a set of explicit edit actions,
including a designated COPY action. We experiment with character alignment and
find that naive, greedy alignment consistently produces strong results for some
languages. Our best system combination is the overall winner of the SIGMORPHON
2017 Shared Task 1 without external resources. At a setting with 100 training
samples, both our approaches, as ensembles of models, outperform the next best
competitor.
