Code and data of the ACL 2020 paper "Word-level Textual Adversarial Attacking as Combinatorial Optimization". [paper]

Please cite our paper if you find it helpful.

@inproceedings{zang2020word,
  title={Word-level Textual Adversarial Attacking as Combinatorial Optimization},
  author={Zang, Yuan and Qi, Fanchao and Yang, Chenghao and Liu, Zhiyuan and Zhang, Meng and Liu, Qun and Sun, Maosong},
  booktitle={Proceedings of ACL},
  year={2020}
}

This repository is mainly contributed by Yuan Zang and Chenghao Yang.

• tensorflow-gpu == 1.14.0
• keras == 2.2.4
• sklearn == 0.0
• anytree == 2.6.0
• nltk == 3.4.5
• OpenHowNet == 0.0.1a8
• pytorch_transformers == 1.0.0
• loguru == 0.3.2

• Download Glove vectors

• Download Stanford POS Tagger

Since data processing and models training may take a lot of time and computing resources, we provide the data and models we use for experiments. You can directly download the data and models we used for related experiments from TsinghuaCloud or Google Drive. The instructions of how to use these files can be found in the README.md files in IMDB/, SNLI/ and SST/.

Please see the README.md files in IMDB/, SNLI/ and SST/ for specific running instructions for each attack models on corresponding downstream tasks.