  Generating adversarial examples for Neural Machine Translation (NMT) with
single Round-Trip Translation (RTT) has achieved promising results by releasing
the meaning-preserving restriction. However, a potential pitfall for this
approach is that we cannot decide whether the generated examples are
adversarial to the target NMT model or the auxiliary backward one, as the
reconstruction error through the RTT can be related to either. To remedy this
problem, we propose a new criterion for NMT adversarial examples based on the
Doubly Round-Trip Translation (DRTT). Specifically, apart from the
source-target-source RTT, we also consider the target-source-target one, which
is utilized to pick out the authentic adversarial examples for the target NMT
model. Additionally, to enhance the robustness of the NMT model, we introduce
the masked language models to construct bilingual adversarial pairs based on
DRTT, which are used to train the NMT model directly. Extensive experiments on
both the clean and noisy test sets (including the artificial and natural noise)
show that our approach substantially improves the robustness of NMT models.
