TY - JOUR AU - Liu, Hui AU - Zhang, Yongzheng AU - Wang, Yipeng AU - Lin, Zheng AU - Chen, Yige PY - 2020/04/03 Y2 - 2024/03/29 TI - Joint Character-Level Word Embedding and Adversarial Stability Training to Defend Adversarial Text JF - Proceedings of the AAAI Conference on Artificial Intelligence JA - AAAI VL - 34 IS - 05 SE - AAAI Technical Track: Natural Language Processing DO - 10.1609/aaai.v34i05.6356 UR - https://ojs.aaai.org/index.php/AAAI/article/view/6356 SP - 8384-8391 AB - <p>Text classification is a basic task in natural language processing, but the small character perturbations in words can greatly decrease the effectiveness of text classification models, which is called character-level adversarial example attack. There are two main challenges in character-level adversarial examples defense, which are out-of-vocabulary words in word embedding model and the distribution difference between training and inference. Both of these two challenges make the character-level adversarial examples difficult to defend. In this paper, we propose a framework which jointly uses the character embedding and the adversarial stability training to overcome these two challenges. Our experimental results on five text classification data sets show that the models based on our framework can effectively defend character-level adversarial examples, and our models can defend 93.19% gradient-based adversarial examples and 94.83% natural adversarial examples, which outperforms the state-of-the-art defense models.</p> ER -