SememePSO-Attack

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

Citation

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.

Requirements

• 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

  General Required Data and Tools

• Download Glove vectors
• Download Stanford POS Tagger

Reproducibility Support

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 IMDB-related experiments from TsinghuaCloud. The instructions of how to use these files can be found in the README.md files in IMDB/, SNLI/ and SST/.

Running Instructions

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