Year: 2022
Type: conference-paper
Abstract: 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 humanbot dialogue settings, with focuses on contextsensitive 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 contextsensitive safety problems.
Language: English
Topic: Speech and dialogue systems
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W3207604419