Interactive Persona Chat for Accurate Response Selection

Abstract

Developing personalized chatbots is crucial in the field of AI, particularly when aiming for dynamic adaptability similar to that of human communication. Traditional methods often overlook the importance of both the speaker's and the responder's personalities and their interaction histories, resulting in lower predictive accuracy. Our solution, INTPChat (Interactive Persona Chat), addresses this limitation. INTPChat builds implicit profiles from extensive utterance histories of both speakers and responders and updates these profiles dynamically to reflect current conversational contexts. By employing a co-attention encoding mechanism, INTPChat aligns current contexts with responses while considering historical interactions. This approach effectively mitigates data sparsity issues by iteratively shifting each context backward in time, allowing for a more granular analysis of long-term interactions. Evaluations on long-term Reddit datasets demonstrate that INTPChat significantly enhances response accuracy and surpasses the performance of state-of-the-art persona chat models.