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Generating Fluent Adversarial Examples for Natural Languages
[article]
2020
arXiv
pre-print
Efficiently building an adversarial attacker for natural language processing (NLP) tasks is a real challenge. Firstly, as the sentence space is discrete, it is difficult to make small perturbations along the direction of gradients. Secondly, the fluency of the generated examples cannot be guaranteed. In this paper, we propose MHA, which addresses both problems by performing Metropolis-Hastings sampling, whose proposal is designed with the guidance of gradients. Experiments on IMDB and SNLI show
arXiv:2007.06174v1
fatcat:kvl555rxpfd7np62fid3bqyuqe