  Labeled sequence transduction is a task of transforming one sequence into
another sequence that satisfies desiderata specified by a set of labels. In
this paper we propose multi-space variational encoder-decoders, a new model for
labeled sequence transduction with semi-supervised learning. The generative
model can use neural networks to handle both discrete and continuous latent
variables to exploit various features of data. Experiments show that our model
provides not only a powerful supervised framework but also can effectively take
advantage of the unlabeled data. On the SIGMORPHON morphological inflection
benchmark, our model outperforms single-model state-of-art results by a large
margin for the majority of languages.
