Domain-Specific Multi-Agent Dialog Policy Learning in Multi-Domain Task-Oriented Scenarios
(3 minutes introduction)
|Li Tang (Tianjin University, China), Yuke Si (Tianjin University, China), Longbiao Wang (Tianjin University, China), Jianwu Dang (Tianjin University, China)|
Traditional dialog policy learning methods train a generic dialog agent to address all situations. However, when the dialog agent encounters a complicated task that involves more than one domain, it becomes difficult to perform concordant actions due to the hybrid information in the multi-domain ontology. Inspired by a real-life scenario at a bank, there are always several specialized departments that deal with different businesses. In this paper, we propose Domain-Specific Multi-Agent Dialog Policy Learning (DSMADPL), in which the dialog system is composed of a set of agents where each agent represents a specialized skill in a particular domain. Every domain-specific agent is first pretrained with supervised learning using a dialog corpus, and then they are jointly improved with multi-agent reinforcement learning. When the dialog system interacts with the user, in each turn the system action is decided by the actions of relevant agents. Experiments conducted on the commonly used MultiWOZ dataset prove the effectiveness of the proposed method, in which dialog success rate increases from 55.0% for the traditional method to 67.2% for our method in multi-domain scenarios.