AdvAug: Robust Adversarial Augmentation for Neural Machine Translation
This addresses the need for more robust translation models without requiring extra data, offering a domain-specific advancement in machine learning.
The paper tackles the problem of improving Neural Machine Translation by proposing AdvAug, an adversarial augmentation method that minimizes vicinal risk over virtual sentences, resulting in up to 4.9 BLEU point improvements over the Transformer on benchmarks like Chinese-English and English-French.
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.