Yuting Yang


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PAD: A Robustness Enhancement Ensemble Method via Promoting Attention Diversity
Yuting Yang | Pei Huang | Feifei Ma | Juan Cao | Jintao Li
Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)

Deep neural networks can be vulnerable to adversarial attacks, even for the mainstream Transformer-based models. Although several robustness enhancement approaches have been proposed, they usually focus on some certain type of perturbation. As the types of attack can be various and unpredictable in practical scenarios, a general and strong defense method is urgently in require. We notice that most well-trained models can be weakly robust in the perturbation space, i.e., only a small ratio of adversarial examples exist. Inspired by the weak robust property, this paper presents a novel ensemble method for enhancing robustness. We propose a lightweight framework PAD to save computational resources in realizing an ensemble. Instead of training multiple models, a plugin module is designed to perturb the parameters of a base model which can achieve the effect of multiple models. Then, to diversify adversarial example distributions among different models, we promote each model to have different attention patterns via optimizing a diversity measure we defined. Experiments on various widely-used datasets and target models show that PAD can consistently improve the defense ability against many types of adversarial attacks while maintaining accuracy on clean data. Besides, PAD also presents good interpretability via visualizing diverse attention patterns.


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近四十年湘方言语音研究的回顾与展望——基于知识图谱绘制和文献计量分析(Review and Prospect of the Phonetic Research of Xiang Dialects in Recent Forty Years:Based on Knowledge Mapping and Bibliometric Analysis)
Yuting Yang (杨玉婷) | Xinzhong Liu (刘新中) | Zhifeng Peng (彭志峰)
Proceedings of the 21st Chinese National Conference on Computational Linguistics