Towards Robust Dialogue State Tracking in Unseen Scenarios: Leveraging Semantic-Independent Expert Mixtures for Zero-Shot Adaptation

Abstract

In recent years, dialogue state tracking (DST) has emerged as a critical component in the development of intelligent conversational agents. As the demand for more adaptable and robust dialogue systems grows, the challenge of effectively tracking user intentions in unseen scenarios becomes increasingly significant. This paper proposes a novel approach to DST by leveraging semantic-independent expert mixtures, enabling zero-shot adaptation to new domains. The proposed framework enhances the flexibility and generalizability of dialogue systems by effectively combining the strengths of various expert models. Our approach shows promise in improving performance across diverse dialogue scenarios while minimizing the need for extensive retraining. Experimental results demonstrate that this method outperforms traditional models, particularly in previously unseen contexts. This research highlights the potential of expert mixture models to provide robust DST solutions in dynamic and unpredictable environments.