  Document-level machine translation faces the challenge of data sparsity due
to its long input length and a small amount of training data, increasing the
risk of learning spurious patterns. To address this challenge, we propose a
target-side augmentation method, introducing a data augmentation (DA) model to
generate many potential translations for each source document. Learning on
these wider range translations, an MT model can learn a smoothed distribution,
thereby reducing the risk of data sparsity. We demonstrate that the DA model,
which estimates the posterior distribution, largely improves the MT
performance, outperforming the previous best system by 2.30 s-BLEU on News and
achieving new state-of-the-art on News and Europarl benchmarks. Our code is
available at https://github.com/baoguangsheng/target-side-augmentation.
