  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.
