A Crowd-based Evaluation of Abuse Response Strategies in Conversational Agents
This addresses the challenge of improving user interactions and safety in conversational AI, though it is incremental as it evaluates existing strategies rather than proposing new ones.
The paper tackled the problem of how conversational agents should respond to verbal abuse by conducting a large-scale crowd-sourced evaluation of response strategies, finding that strategies like 'polite refusal' score highly, while demographic factors and abuse severity influence perceptions, and data-driven models lag behind rule-based or commercial systems in appropriateness.
How should conversational agents respond to verbal abuse through the user? To answer this question, we conduct a large-scale crowd-sourced evaluation of abuse response strategies employed by current state-of-the-art systems. Our results show that some strategies, such as "polite refusal" score highly across the board, while for other strategies demographic factors, such as age, as well as the severity of the preceding abuse influence the user's perception of which response is appropriate. In addition, we find that most data-driven models lag behind rule-based or commercial systems in terms of their perceived appropriateness.