Mask language model has been successfully developed to build a transformer for robust language understanding. The transformer-based language model has achieved excellent results in various downstream applications. However, typical mask language model is trained by predicting the randomly masked words and is used to transfer the knowledge from rich-resource pre-training task to low-resource downstream tasks. This study incorporates a rich contextual embedding from pre-trained model and strengthens the attention layers for sequence-to-sequence learning. In particular, an adversarial mask mechanism is presented to deal with the shortcoming of random mask and accordingly enhance the robustness in word prediction for language understanding. The adversarial mask language model is trained in accordance with a minimax optimization over the word prediction loss. The worst-case mask is estimated to build an optimal and robust language model. The experiments on two machine translation tasks show the merits of the adversarial mask transformer.