Developing an Uncertainty-Aware Empathetic Conversational System

This document is a model and instructions for LATEX. Natural empathetic dialogue requires an understanding of emotion, intention, and context. In empathetic conversational systems, accurate classification of emotion, intention, and understanding of context contribute to a more natural and effective dialogue between the system and the user. However, due to the subjective nature of these concepts and the lack of contextual cues, the resulting models are susceptible to uncertain outputs, causing misunderstanding and inappropriate response generation in empathetic conversational systems. This research proposal aims to address this issue by proposing the use of Bayesian inference methods to estimate and reduce uncertainty in emotion, intention, and context modeling tasks. By incorporating uncertainty estimates into the downstream task of response generation, the proposed uncertainty-aware approach is expected to improve the performance of empathetic dialogue generation tasks significantly. The potential impact of this research is providing a more natural, effective, and engaging user experience in conversational systems, with applications in areas such as mental health, customer service, and education.