  Morphological reinflection is the task of generating a target form given a
source form, a source tag and a target tag. We propose a new way of modeling
this task with neural encoder-decoder models. Our approach reduces the amount
of required training data for this architecture and achieves state-of-the-art
results, making encoder-decoder models applicable to morphological reinflection
even for low-resource languages. We further present a new automatic correction
method for the outputs based on edit trees.
