论文标题
TextAttack：在设计NLP的Python框架时学到了教训
TextAttack: Lessons learned in designing Python frameworks for NLP
论文作者
论文摘要
TextAttack是一种开源Python工具包，用于NLP中的对抗性攻击，对抗性训练和数据增强。 TextAttack将NLP对抗性攻击文献中的15篇论文团结成一个框架，许多组件在攻击中都重复了。该框架使研究人员和开发人员都可以测试和研究其NLP模型的弱点。要构建这样的开源NLP工具包需要解决一些常见问题：我们如何使用户能够从不同的深度学习框架中提供模型？我们如何构建工具来支持尽可能多的不同数据集？我们分享我们的见解，以开发一个写得很好的，有据可查的NLP Python框架，希望它们可以帮助未来的类似包装的开发。
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 attack literature into a single framework, with many components reused across attacks. This framework allows both researchers and developers to test and study the weaknesses of their NLP models. To build such an open-source NLP toolkit requires solving some common problems: How do we enable users to supply models from different deep learning frameworks? How can we build tools to support as many different datasets as possible? We share our insights into developing a well-written, well-documented NLP Python framework in hope that they can aid future development of similar packages.
