On Adversarial Examples for Character-Level Neural Machine Translation

Understand

Evaluating on adversarial examples has become a standard procedure to measure robustness of deep learning models.

• Due to the difficulty of creating white-box adversarial examples for discrete text input, most analyses of the robustness of NLP models have been done through black-box adversarial examples.
• We investigate adversarial examples for character-level neural machine translation (NMT), and contrast black-box adversaries with a novel white-box adversary, which employs differentiable string-edit operations to rank adversarial changes.
• We propose two novel types of attacks which aim to remove or change a word in a translation, rather than simply break the NMT.