README.md

Searching for a Search Method: Benchmarking Search Algorithms for Generating NLP Adversarial Examples

This repo contains the code and results for paper "Searching for a Search Method: Benchmarking Search Algorithms for Generating NLP Adversarial Examples", which will appear in the EMNLP 2020 Blackbox NLP Workshop track proceedings.

Note that all the experiment was carried using TextAttack, which is a Python framework for adversarial attacks, data augmentation, and model training in NLP.

Attack Recipes

In TextAttack, an adversarial attack is composed of four components: a transformation, a set of constraints, a goal function, and a search algorithm. An attack recipe is a specification of these four components that TextAttack uses to create the adversarial attacks. Each recipe is a Python file that is imported by TextAttack on the fly.

Attack recipes for each experiment are in recipes folder, organized by the search space (i.e. transformation and constraints) and search algorithm. Note that the are two version of each recipes: "strict" and "lax". Recipes under lax have weaker threshold values for constraints. In our paper, we experimented with both weak and strict constraints, and present results of experiments with strict constraints.

Results

TextAttack can output both .txt and .csv logs for each run. Result of each experiment is in results, organized by the victim model, search space, and search algorithm. Similar to recipes, there are results for both strict and weak constraint settings. Our paper mainly deals with those under strict constraint settings.

Reproducing Experiments

To reproduce these experiments, first install TextAttack. Then, you can run run_experiment.py script to run each experiment.

For example, to attack a BERT-MR model using beam search of beam width 4 and WordNet transformation (and its corresponding constraints), you can run the following:

python run_experiment.py --model bert-base-uncased-mr --recipe-path ./recipes/word-swap-wordnet/strict/beam-search/beam4-recipe.py --txt-log-path . --csv-log-path

To run all the 15 experiments, you can use python grid_run.py.

Evaluation

Evaluation of results are done in autoevaluation.ipynb notebook.

Citing

To cite this work, please use

@misc{yoo2020searching,
      title={Searching for a Search Method: Benchmarking Search Algorithms for Generating NLP Adversarial Examples}, 
      author={Jin Yong Yoo and John X. Morris and Eli Lifland and Yanjun Qi},
      year={2020},
      eprint={2009.06368},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}