Pengwei Zhan


2022

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PARSE: An Efficient Search Method for Black-box Adversarial Text Attacks
Pengwei Zhan | Chao Zheng | Jing Yang | Yuxiang Wang | Liming Wang | Yang Wu | Yunjian Zhang
Proceedings of the 29th International Conference on Computational Linguistics

Neural networks are vulnerable to adversarial examples. The adversary can successfully attack a model even without knowing model architecture and parameters, i.e., under a black-box scenario. Previous works on word-level attacks widely use word importance ranking (WIR) methods and complex search methods, including greedy search and heuristic algorithms, to find optimal substitutions. However, these methods fail to balance the attack success rate and the cost of attacks, such as the number of queries to the model and the time consumption. In this paper, We propose PAthological woRd Saliency sEarch (PARSE) that performs the search under dynamic search space following the subarea importance. Experiments show that PARSE can achieve comparable attack success rates to complex search methods while saving numerous queries and time, e.g., saving at most 74% of queries and 90% of time compared with greedy search when attacking the examples from Yelp dataset. The adversarial examples crafted by PARSE are also of high quality, highly transferable, and can effectively improve model robustness in adversarial training.

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Mitigating the Inconsistency Between Word Saliency and Model Confidence with Pathological Contrastive Training
Pengwei Zhan | Yang Wu | Shaolei Zhou | Yunjian Zhang | Liming Wang
Findings of the Association for Computational Linguistics: ACL 2022

Neural networks are widely used in various NLP tasks for their remarkable performance. However, the complexity makes them difficult to interpret, i.e., they are not guaranteed right for the right reason. Besides the complexity, we reveal that the model pathology - the inconsistency between word saliency and model confidence, further hurts the interpretability. We show that the pathological inconsistency is caused by the representation collapse issue, which means that the representation of the sentences with tokens in different saliency reduced is somehow collapsed, and thus the important words cannot be distinguished from unimportant words in terms of model confidence changing. In this paper, to mitigate the pathology and obtain more interpretable models, we propose Pathological Contrastive Training (PCT) framework, which adopts contrastive learning and saliency-based samples augmentation to calibrate the sentences representation. Combined with qualitative analysis, we also conduct extensive quantitative experiments and measure the interpretability with eight reasonable metrics. Experiments show that our method can mitigate the model pathology and generate more interpretable models while keeping the model performance. Ablation study also shows the effectiveness.

2021

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Multimodal Fusion with Co-Attention Networks for Fake News Detection
Yang Wu | Pengwei Zhan | Yunjian Zhang | Liming Wang | Zhen Xu
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021