  Adversarial attacking aims to fool deep neural networks with adversarial
examples. In the field of natural language processing, various textual
adversarial attack models have been proposed, varying in the accessibility to
the victim model. Among them, the attack models that only require the output of
the victim model are more fit for real-world situations of adversarial
attacking. However, to achieve high attack performance, these models usually
need to query the victim model too many times, which is neither efficient nor
viable in practice. To tackle this problem, we propose a reinforcement learning
based attack model, which can learn from attack history and launch attacks more
efficiently. In experiments, we evaluate our model by attacking several
state-of-the-art models on the benchmark datasets of multiple tasks including
sentiment analysis, text classification and natural language inference.
Experimental results demonstrate that our model consistently achieves both
better attack performance and higher efficiency than recently proposed baseline
methods. We also find our attack model can bring more robustness improvement to
the victim model by adversarial training. All the code and data of this paper
will be made public.
