Multi-space Variational Encoder-Decoders for Semi-supervised Labeled Sequence Transduction
Chunting Zhou, Graham Neubig · 2017
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 encoderdecoders, 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-ofart results by a large margin for the majority of languages.1