TextAttack: A Framework for Adversarial Attacks, Data Augmentation, and Adversarial Training in NLP (2005.05909v4)

Abstract: 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 NLP attacks and utilize them to improve model performance. This paper introduces TextAttack, a Python framework for adversarial attacks, data augmentation, and adversarial training in NLP. TextAttack builds attacks from four components: a goal function, a set of constraints, a transformation, and a search method. TextAttack's modular design enables researchers to easily construct attacks from combinations of novel and existing components. TextAttack provides implementations of 16 adversarial attacks from the literature and supports a variety of models and datasets, including BERT and other transformers, and all GLUE tasks. TextAttack also includes data augmentation and adversarial training modules for using components of adversarial attacks to improve model accuracy and robustness. TextAttack is democratizing NLP: anyone can try data augmentation and adversarial training on any model or dataset, with just a few lines of code. Code and tutorials are available at https://github.com/QData/TextAttack.

• The paper introduces a modular NLP framework that decomposes adversarial attacks into four components: goal function, constraints, transformation, and search method.
• The paper details 16 adversarial attack implementations across popular models like BERT and diverse datasets such as GLUE for benchmarking and strategy development.
• The paper demonstrates that integrated adversarial training and data augmentation modules can significantly boost model resilience and performance, especially for smaller datasets.

TextAttack: A Modular Framework for NLP Adversarial Attacks

Despite significant advancements in NLP, the vulnerability of models to adversarial attacks remains a critical area of investigation. The paper "TextAttack: A Framework for Adversarial Attacks, Data Augmentation, and Adversarial Training in NLP" addresses this challenge by introducing a comprehensive Python framework designed to facilitate adversarial attacks, data augmentation, and adversarial training for NLP models.

TextAttack is an innovative framework that unifies various adversarial attack methodologies through a modular approach, decomposing attacks into four primary components: goal function, constraints, transformation, and search method. This design allows for straightforward composition and execution of attacks, enabling researchers to benchmark, compare, and develop novel attack strategies efficiently.

The authors provide implementations for 16 documented adversarial attacks, encompassing popular models like BERT and other transformers, alongside diverse datasets, particularly those associated with the GLUE benchmark. The framework's integration with HuggingFace libraries further amplifies its utility, allowing users to evaluate numerous pre-trained models seamlessly.

TextAttack also serves to enhance model robustness and accuracy through its data augmentation and adversarial training modules. By employing transformation and constraint modules from attacks, new data samples can be generated to augment training datasets. This aspect democratizes the accessibility of adversarial training, empowering researchers to apply these techniques with minimal coding overhead.

Empirical results show that utilizing TextAttack's augmentation capabilities can yield immediate improvements in model performance, particularly in scenarios involving smaller datasets. Additionally, adversarial training facilitated by TextAttack has been demonstrated to significantly enhance model resilience to specific attacks.

The framework's capability to evaluate custom models, train standard architectures, and apply data augmentation underscores its potential as a pivotal tool for advancing NLP resilience research. Furthermore, the open-source nature of TextAttack encourages community engagement and continuous improvement, fostering collaboration and innovation in the field.

In conclusion, TextAttack offers a streamlined, extensible platform for adversarial research in NLP, providing researchers with a robust toolkit to assess and enhance model robustness. By integrating adversarial attacks, data augmentation, and adversarial training into a unified framework, TextAttack establishes itself as a valuable resource for the NLP research community. As new adversarial techniques emerge, TextAttack is poised to adapt and incorporate novel components, further supporting ongoing research in model robustness and performance optimization.