Quick Back-Translation for Unsupervised Machine Translation
Benjamin Brimacombe Jiawei Zhou
The field of unsupervised machine translation has seen significant
advancement from the marriage of the Transformer and the back-translation
algorithm. The Transformer is a powerful generative model, and back-translation
leverages Transformer's high-quality translations for iterative
self-improvement. However, the Transformer is encumbered by the run-time of
autoregressive inference during back-translation, and back-translation is
limited by a lack of synthetic data efficiency. We propose a two-for-one
improvement to Transformer back-translation: Quick Back-Translation (QBT). QBT
re-purposes the encoder as a generative model, and uses encoder-generated
sequences to train the decoder in conjunction with the original autoregressive
back-translation step, improving data throughput and utilization. Experiments
on various WMT benchmarks demonstrate that a relatively small number of
refining steps of QBT improve current unsupervised machine translation models,
and that QBT dramatically outperforms standard back-translation only method in
terms of training efficiency for comparable translation qualities.