This transformation translates a given English sentence into German and back to English.
This transformation acts like a light paraphraser. Multiple variations can be easily created via changing parameters like the language as well as the translation models which are available in plenty.
This perturbation would benefit all tasks which have a sentence/paragraph/document as input like text classification, text generation, etc.
Zhenhao Li and Lucia Specia. 2019. Improving neural machine translation robustness via data augmentation: Beyond backtranslation. Amane Sugiyama and Naoki Yoshinaga. 2019. Data augmentation using back-translation for context-aware neural machine translation.
The transformation's outputs are dependent on the accuracy of the individual translation models and generally would generate simpler text or more popularly used text.