A Stochastic Decoder for Neural Machine Translation

ACL 2018

A Stochastic Decoder for Neural Machine Translation

Jan 28, 2021
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Abstract: 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 vari-ational inference for training deep generative models. Experiments on several different language pairs demonstrate that the model consistently improves over strong baselines. Authors: Philip Schulz, Wilker Aziz, Trevor Cohn (Amazon Research, University of Amsterdam, University of Melbourne)

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