  Dialogue systems require a great deal of different but complementary
expertise to assist, inform, and entertain humans. For example, different
domains (e.g., restaurant reservation, train ticket booking) of goal-oriented
dialogue systems can be viewed as different skills, and so does ordinary
chatting abilities of chit-chat dialogue systems. In this paper, we propose to
learn a dialogue system that independently parameterizes different dialogue
skills, and learns to select and combine each of them through Attention over
Parameters (AoP). The experimental results show that this approach achieves
competitive performance on a combined dataset of MultiWOZ, In-Car Assistant,
and Persona-Chat. Finally, we demonstrate that each dialogue skill is
effectively learned and can be combined with other skills to produce selective
responses.
