Reviews: Generative Neural Machine Translation

Summary This paper proposes a generative latent variable model for neural machine translation, where inference is performed with variational inference. This extends the work of Zhang et al., 2016, who proposed a conditional model with variational inference. The advantage of the generative model is to force the latent variable to capture more of the semantics of the sentence than the conditional model was able to do. The main disadvantage of this approach is that the value of the latent variable has to be infered during decoding (based on candidate generations). The paper also shows that a version of this model can be trained in a multilingual setting, that monolingual data can be used as semi-supervised training, and that the inference algorithm can be extended to perform translation with missing words.