  Word alignments identify translational correspondences between words in a
parallel sentence pair and are used, for instance, to learn bilingual
dictionaries, to train statistical machine translation systems or to perform
quality estimation. Variational autoencoders have been recently used in various
of natural language processing to learn in an unsupervised way latent
representations that are useful for language generation tasks. In this paper,
we study these models for the task of word alignment and propose and assess
several evolutions of a vanilla variational autoencoders. We demonstrate that
these techniques can yield competitive results as compared to Giza++ and to a
strong neural network alignment system for two language pairs.

Computation and Language;Machine Learning