Papers › AdvAug: Robust Adversarial Augmentation for Neural Machine Translation

AdvAug: Robust Adversarial Augmentation for Neural Machine Translation

21 Jun 2020ACL 2020 6arXiv:2006.11834archive 2025-07-28

Yong Cheng, Lu Jiang, Wolfgang Macherey, Jacob Eisenstein

In this paper, we propose a new adversarial augmentation method for Neural Machine Translation (NMT). The main idea is to minimize the vicinal risk over virtual sentences sampled from two vicinity distributions, of which the crucial one is a novel vicinity distribution for adversarial sentences that describes a smooth interpolated embedding space centered around observed training sentence pairs. We then discuss our approach, AdvAug, to train NMT models using the embeddings of virtual sentences in sequence-to-sequence learning. Experiments on Chinese-English, English-French, and English-German translation benchmarks show that AdvAug achieves significant improvements over the Transformer (up to 4.9 BLEU points), and substantially outperforms other data augmentation techniques (e.g. back-translation) without using extra corpora.

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