Adversarial Language Games for Advanced Natural Language Intelligence

By Yuan Yao, Haoxiang Zhong, Zhengyan Zhang, Xu Han, Xiaozhi Wang, Kai Zhang, Chaojun Xiao, Guoyang Zeng, Zhiyuan Liu, Maosong Sun. 2019

8 citations recorded by the source corpus.

We study the problem of adversarial language games, in which multiple agents with conflicting goals compete with each other via natural language interactions. While adversarial language games are ubiquitous in human activities, little attention has been devoted to this field in natural language processing. In this work, we propose a challenging adversarial language game called Adversarial Taboo as an example, in which an attacker and a defender compete around a target word. The attacker is tasked with inducing the defender to utter the target word invisible to the defender, while the defender is tasked with detecting the target word before being induced by the attacker. In Adversarial Taboo, a successful attacker and defender need to hide or infer the intention, and induce or defend during conversations. This requires several advanced language abilities, such as adversarial pragmatic reasoning and goal-oriented language interactions in open domain, which will facilitate many downstream NLP tasks. To instantiate the game, we create a game environment and a competition platform. Comprehensive experiments on several baseline attack and defense strategies show promising and interesting results, based on which we discuss some directions for future research.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Zhiyuan Liu
2. Minlie Huang
3. Maosong Sun
4. Jie Tang
5. Xu Han
6. Xipeng Qiu
7. Ji-rong Wen
8. Wayne Xin Zhao

In Computer Vision and Pattern Recognition

1. Zhiyuan Liu
2. Ji-rong Wen
3. Yang Liu
4. Xu Han
5. Wayne Xin Zhao
6. Minlie Huang
7. Jie Tang
8. Xiao Liu

Citations in the corpus

• Pre-Trained Models: Past, Present and Future (2021, background): “Adversarial defenses (Si et al., 2020; Yao et al., 2021; Li and Qiu, 2021) for PTMs are also promising, which can improve the robustness of PTMs and make them immune against adversarial attacks.”