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On the Safety of Conversational Models: Taxonomy, Dataset, and Benchmark by Sun, Hao; Xu, Guangxuan; Deng, Jiawen; Cheng, Jiale; Zheng, Chujie; Zhou, Hao; Peng, Nanyun; Zhu, Xiaoyan; Huang, Minlie is a scholarly article available to read on EtoBox.

What is On the Safety of Conversational Models: Taxonomy, Dataset, and Benchmark about?

Dialogue safety problems severely limit the real-world deployment of neural conversational models and have attracted great research interests recently. However, dialogue safety problems remain under-defined and the corresponding dataset is scarce. We propose a taxonomy for dialogue safety specifically designed to capture unsafe behaviors in human-bot dialogue settings, with focuses on context-sensitive unsafety, which is under-explored in prior works. To spur research in this direction, we compile DiaSafety, a dataset with rich context-sensitive unsafe examples. Experiments show that existing safety guarding tools fail severely on our dataset. As a remedy, we train a dialogue safety classifier to provide a strong baseline for context-sensitive dialogue unsafety detection. With our classifier, we perform safety evaluations on popular conversational models and show that existing dialogue systems still exhibit concerning context-sensitive safety problems.

Author

Sun, Hao; Xu, Guangxuan; Deng, Jiawen; Cheng, Jiale; Zheng, Chujie; Zhou, Hao; Peng, Nanyun; Zhu, Xiaoyan; Huang, Minlie

Published

2021

Language

EN