In Neural Machine Translation (NMT), data augmentation methods such as
back-translation have proven their effectiveness in improving translation
performance. In this paper, we propose a novel data augmentation approach for
NMT, which is independent of any additional training data. Our approach, AdMix,
consists of two parts: 1) introduce faint discrete noise (word replacement,
word dropping, word swapping) into the original sentence pairs to form
augmented samples; 2) generate new synthetic training data by softly mixing the
augmented samples with their original samples in training corpus. Experiments
on three translation datasets of different scales show that AdMix achieves
signifi cant improvements (1.0 to 2.7 BLEU points) over strong Transformer
baseline. When combined with other data augmentation techniques (e.g.,
back-translation), our approach can obtain further improvements.

本文提出了一种新颖的 NMT 数据增强方法 ——AdMix，将弱离散噪声引入原始句对，生成新的合成训练数据，并将其与原始样本软混合，实验证明，该方法可显著提高翻译效果，特别是与 back-translation 等方法联合使用时，改进效果进一步增强。

AdMix：一种混合采样数据增强方法用于神经机器翻译

AdMix: A Mixed Sample Data Augmentation Method for Neural Machine Translation

Deep neural networks are known to be extremely vulnerable to adversarial
examples under white-box setting. Moreover, the malicious adversaries crafted
on the surrogate (source) model often exhibit black-box transferability on
other models with the same learning task but having different architectures.
Recently, various methods are proposed to boost the adversarial
transferability, among which the input transformation is one of the most
effective approaches. We investigate in this direction and observe that
existing transformations are all applied on a single image, which might limit
the adversarial transferability. To this end, we propose a new input
transformation based attack method called Admix that considers the input image
and a set of images randomly sampled from other categories. Instead of directly
calculating the gradient on the original input, Admix calculates the gradient
on the input image admixed with a small portion of each add-in image while
using the original label of the input to craft more transferable adversaries.
Empirical evaluations on standard ImageNet dataset demonstrate that Admix could
achieve significantly better transferability than existing input transformation
methods under both single model setting and ensemble-model setting. By
incorporating with existing input transformations, our method could further
improve the transferability and outperforms the state-of-the-art combination of
input transformations by a clear margin when attacking nine advanced defense
models under ensemble-model setting. Code is available at
this https URL

该研究旨在提高对抗转移性能，在理论和实验的基础上设计了一种称为 Admix 的输入变换攻击方法，该方法能够利用附加图像集合进行攻击并优于现有方法。