Attention over Parameters for Dialogue Systems
This work addresses the need for more adaptable and modular dialogue systems, but it is incremental as it builds on existing attention mechanisms and datasets.
The paper tackles the problem of building dialogue systems that require diverse skills by proposing a method that independently parameterizes different dialogue skills and uses attention to select and combine them, achieving competitive performance on a combined dataset including MultiWOZ, In-Car Assistant, and Persona-Chat.
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.