Back-translation has become a commonly employed heuristic for semi-supervised neural machine translation. The technique is both straightforward to apply and has led to state-of-the-art results. In this work, we offer a principled interpretation of back-translation as approximate inference in a generative model of bitext and show how the standard implementation of back-translation corresponds to a single iteration of the wake-sleep algorithm in our proposed model. Moreover, this interpretation suggests a natural iterative generalization, which we demonstrate leads to further improvement of up to 1.6 BLEU.
@article{arxiv.1806.04402,
title = {Explaining and Generalizing Back-Translation through Wake-Sleep},
author = {Ryan Cotterell and Julia Kreutzer},
journal= {arXiv preprint arXiv:1806.04402},
year = {2018}
}