Robustness-aware Word Embedding Improves Certified Robustness to Adversarial Word Substitutions

This repository contains Pytorch implementations of the paper Robustness-Aware Word Embedding Improves Certified Robustness to Adversarial Word Substitutions (Findings of ACL 2023).

Environment

• Pytorch 1.11.0+cu113
• NLTK 3.7
• Keras 2.2.5

Datasets

Download data dependencies by running the provided script:

./download_deps.sh

If you already have GloVe vectors on your system, it may be more convenient to comment out the part of download_deps.sh that downloads GloVe, and instead add a symlink to the directory containing the GloVe vectors at data/glove.

How to run

We provide complete training scripts to reproduce the results in our paper. These scripts are in the command folder. For example, to reproduce the result of IMDB dataset with EIBC+IBP training method, simply run

bash command/eibc+ibp/eibc+ibp_imdb_textcnn.sh

In the command folder

• command/eibc provides scripts of EIBC+Normal training method.
• command/eibc+ibp provides scripts of EIBC+IBP training method.
• command/ibp provides scripts of our implementation of IBP method.
• command/half_syns provides scripts of unseen word substitutions experiment.
• command/ga.sh provides script of genetic attack.

Implementation

Part of the codes in this repo are borrowed/modified from [1], [2].

References:

[1] https://github.com/robinjia/certified-word-sub

[2] https://github.com/JHL-HUST/FTML