  We present a neural model for morphological inflection generation which
employs a hard attention mechanism, inspired by the nearly-monotonic alignment
commonly found between the characters in a word and the characters in its
inflection. We evaluate the model on three previously studied morphological
inflection generation datasets and show that it provides state of the art
results in various setups compared to previous neural and non-neural
approaches. Finally we present an analysis of the continuous representations
learned by both the hard and soft attention \cite{bahdanauCB14} models for the
task, shedding some light on the features such models extract.
