Minhao Cheng


Evaluating and Enhancing the Robustness of Neural Network-based Dependency Parsing Models with Adversarial Examples
Xiaoqing Zheng | Jiehang Zeng | Yi Zhou | Cho-Jui Hsieh | Minhao Cheng | Xuanjing Huang
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Despite achieving prominent performance on many important tasks, it has been reported that neural networks are vulnerable to adversarial examples. Previously studies along this line mainly focused on semantic tasks such as sentiment analysis, question answering and reading comprehension. In this study, we show that adversarial examples also exist in dependency parsing: we propose two approaches to study where and how parsers make mistakes by searching over perturbations to existing texts at sentence and phrase levels, and design algorithms to construct such examples in both of the black-box and white-box settings. Our experiments with one of state-of-the-art parsers on the English Penn Treebank (PTB) show that up to 77% of input examples admit adversarial perturbations, and we also show that the robustness of parsing models can be improved by crafting high-quality adversaries and including them in the training stage, while suffering little to no performance drop on the clean input data.


On the Robustness of Self-Attentive Models
Yu-Lun Hsieh | Minhao Cheng | Da-Cheng Juan | Wei Wei | Wen-Lian Hsu | Cho-Jui Hsieh
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

This work examines the robustness of self-attentive neural networks against adversarial input perturbations. Specifically, we investigate the attention and feature extraction mechanisms of state-of-the-art recurrent neural networks and self-attentive architectures for sentiment analysis, entailment and machine translation under adversarial attacks. We also propose a novel attack algorithm for generating more natural adversarial examples that could mislead neural models but not humans. Experimental results show that, compared to recurrent neural models, self-attentive models are more robust against adversarial perturbation. In addition, we provide theoretical explanations for their superior robustness to support our claims.

Evaluating and Enhancing the Robustness of Dialogue Systems: A Case Study on a Negotiation Agent
Minhao Cheng | Wei Wei | Cho-Jui Hsieh
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)

Recent research has demonstrated that goal-oriented dialogue agents trained on large datasets can achieve striking performance when interacting with human users. In real world applications, however, it is important to ensure that the agent performs smoothly interacting with not only regular users but also those malicious ones who would attack the system through interactions in order to achieve goals for their own advantage. In this paper, we develop algorithms to evaluate the robustness of a dialogue agent by carefully designed attacks using adversarial agents. Those attacks are performed in both black-box and white-box settings. Furthermore, we demonstrate that adversarial training using our attacks can significantly improve the robustness of a goal-oriented dialogue system. On a case-study of the negotiation agent developed by (Lewis et al., 2017), our attacks reduced the average advantage of rewards between the attacker and the trained RL-based agent from 2.68 to -5.76 on a scale from -10 to 10 for randomized goals. Moreover, we show that with the adversarial training, we are able to improve the robustness of negotiation agents by 1.5 points on average against all our attacks.