UniDS: A Unified Dialogue System for Chit-Chat and Task-oriented Dialogues
This addresses the need for more natural human-AI interaction by enabling smooth switching between chatting and task completion, though it is incremental as it builds on existing methods without new parameters.
The authors tackled the problem of separate systems for chit-chat and task-oriented dialogues by proposing UniDS, a unified dialogue system that handles both types comparably to pure chit-chat systems and outperforms state-of-the-art task-oriented systems.
With the advances in deep learning, tremendous progress has been made with chit-chat dialogue systems and task-oriented dialogue systems. However, these two systems are often tackled separately in current methods. To achieve more natural interaction with humans, a dialogue agent needs to be capable of both chatting and accomplishing tasks. To this end, we propose a unified dialogue system (UniDS) with the two aforementioned skills. In particular, we design a unified dialogue data schema, compatible for both chit-chat and task-oriented dialogues, and we train UniDS with mixed dialogue data from a pretrained chit-chat dialogue model. Without adding extra parameters to SOTA baselines, UniDS can alternatively handle chit-chat and task-oriented dialogues in a unified framework. Experimental results demonstrate that the proposed UniDS works comparably well as the pure chit-chat system, and it outperforms state-of-the-art task-oriented dialogue systems. More importantly, UniDS achieves better robustness as it is able to smoothly switch between two types of dialogues. These results demonstrate the feasibility and potential of building an one-for-all dialogue system.