174 citations · 444 across the 27 of their papers we have counts for
5 papers · 1 filter
White-box Testing of NLP models with Mask Neuron Coverage
Arshdeep Sekhon, Yangfeng Ji, Matthew B. Dwyer +1
Recent literature has seen growing interest in using black-box strategies like CheckList for testing the behavior of NLP models. Research on white-box testing has developed a numbe…
Perturbing Inputs for Fragile Interpretations in Deep Natural Language Processing
Sanchit Sinha, Hanjie Chen, Arshdeep Sekhon +2
Interpretability methods like Integrated Gradient and LIME are popular choices for explaining natural language model predictions with relative word importance scores. These interpr…
Towards Improving Adversarial Training of NLP Models
Jin Yong Yoo, Yanjun Qi
Adversarial training, a method for learning robust deep neural networks, constructs adversarial examples during training. However, recent methods for generating NLP adversarial exa…
Searching for a Search Method: Benchmarking Search Algorithms for Generating NLP Adversarial Examples
Jin Yong Yoo, John X. Morris, Eli Lifland +1
We study the behavior of several black-box search algorithms used for generating adversarial examples for natural language processing (NLP) tasks. We perform a fine-grained analysi…
TextAttack: A Framework for Adversarial Attacks, Data Augmentation, and Adversarial Training in NLP
John X. Morris, Eli Lifland, Jin Yong Yoo +3
While there has been substantial research using adversarial attacks to analyze NLP models, each attack is implemented in its own code repository. It remains challenging to develop…