GradBot - A unified dialogue state tracking and dialogue response model for Task Oriented Dialogues (TOD) and Open Domain Dialogues (ODD)

Abstract

The system for task-oriented dialogue domain requires classifying user intent and replying to a specific goal domain. Within the task-oriented sub-module, the Dialogue State Tracker (DST) is well-known as a variety processing tracker. Nonetheless, current DST models tend to focus solely on task-oriented domains (ToD), resulting in constrained performance when deployed in varied scenarios. Besides, current dialogue response models of previous studies achieved quite poor results because the responses were not natural and fluent. In this paper, we propose GradBot, a unified system including DST model vs response model that predicts both types of tasks, task-oriented dialogue (TOD) and open domain dialogue (ODD). Our model leverages the recent advances in prompt engineering and conditional generation to perform zero-shot learning. After experiments, GradBot has achieved an 88.6% and 82.5% score on Joint Goal Accuracy metrics when evaluating the Scheme-Guided Dialogue (SGD) and FusedChat test sets correspondingly, demonstrating the adaptation ability for multi-domains.