Conference Paper Published

Abstract

For a conversation to help and support, speakers should maintain an “effect-effort" trade-off. As outlined in the gist of “Cognitive Relevance Principle", helpful speakers should optimize the “cognitive relevance" through maximizing the “cognitive effects" and minimizing the “processing effort" imposed on listeners. Although preference learning methods provide a boon for studies concerning “effect-optimization", none have delved into “effort-optimization" which is pivotal to the acquisition of “optimal relevance" for emotional support conversation agents. To address this gap, we integrate the "Cognitive Relevance Principle" into emotional support agents in the environment of multi-turn conversation. The results demonstrate a significant and robust improvement against the baseline systems with respect to response quality, human-likedness, and supportiveness. This study offers compelling evidence for the effectiveness of the "Relevance Principle" in generating human-like, helpful, and harmless emotional support conversations. The source code will be available at https://github.com/CN-Eyetk/VLESA-ORL.git.