Quick Back-Translation for Unsupervised Machine Translation

Benjamin Brimacombe; Jiawei Zhou

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arXiv · 2312.00912

Quick Back-Translation for Unsupervised Machine Translation

Benjamin Brimacombe·Jiawei Zhou

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Abstract

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.

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BibTeX RIS

Benjamin Brimacombe, Jiawei Zhou. 2023-12-01. Quick Back-Translation for Unsupervised Machine Translation. https://arxiv.org/abs/2312.00912

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