  We present a semi-supervised way of training a character-based
encoder-decoder recurrent neural network for morphological reinflection, the
task of generating one inflected word form from another. This is achieved by
using unlabeled tokens or random strings as training data for an autoencoding
task, adapting a network for morphological reinflection, and performing
multi-task training. We thus use limited labeled data more effectively,
obtaining up to 9.9% improvement over state-of-the-art baselines for 8
different languages.
