Aziz, W.

Cohn, T.

Schulz, P.

English

The process of translation is ambiguous, in that there are typically many valid translations for a given sentence. This gives rise to significant variation in parallel corpora, however, most current models of machine translation do not account for this variation, instead treating the problem as a deterministic process. To this end, we present a deep generative model of machine translation which incorporates a chain of latent variables, in order to account for local lexical and syntactic variation in parallel corpora. We provide an in-depth analysis of the pitfalls encountered in variational inference for training deep generative models. Experiments on several different language pairs demonstrate that the model consistently improves over strong baselines.<br/

International Migration, Integration and Social Cohesion online publications

A Stochastic Decoder for Neural Machine Translation

https://pure.uva.nl/ws/files/40250609/P18_1115.pdf

A Stochastic Decoder for Neural Machine Translation

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International Migration, Integration and Social Cohesion online publications