activity
20202022
most citedTextAttack: A Framework for Adversarial Attacks, Data Augmentation, and Adversarial Training in NLP

60 citations · 68 across the 4 of their papers we have counts for

collaborators

5 papers

cs.CL20222 cited

Meet Your Favorite Character: Open-domain Chatbot Mimicking Fictional Characters with only a Few Utterances

Seungju Han, Beomsu Kim, Jin Yong Yoo +4

In this paper, we consider mimicking fictional characters as a promising direction for building engaging conversation models. To this end, we present a new practical task where onl…

cs.CL20215 cited

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…

cs.CL2020

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…

cs.SE20201 cited

TextAttack: Lessons learned in designing Python frameworks for NLP

John X. Morris, Jin Yong Yoo, Yanjun Qi

TextAttack is an open-source Python toolkit for adversarial attacks, adversarial training, and data augmentation in NLP. TextAttack unites 15+ papers from the NLP adversarial attac…

cs.CL202060 cited

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…