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