Dynamic Fusion Network for Multi-Domain End-to-end Task-Oriented Dialog

Recent studies have shown remarkable success in end-to-end task-oriented dialog system. However, most neural models rely on large training data, which are only available for a certain number of task domains, such as navigation and scheduling. This makes it difficult to scalable for a new domain with limited labeled data. However, there has been relatively little research on how to effectively use data from all domains to improve the performance of each domain and also unseen domains. To this end, we investigate methods that can make explicit use of domain knowledge and introduce a shared-private network to learn shared and specific knowledge. In addition, we propose a novel Dynamic Fusion Network (DF-Net) which automatically exploit the relevance between the target domain and each domain. Results show that our model outperforms existing methods on multi-domain dialogue, giving the state-of-the-art in the literature. Besides, with little training data, we show its transferability by outperforming prior best model by 13.9% on average. * Email corresponding. Address Distance POI type POI Traffic info 5672 barringer street 5 miles certain address 5672 barringer street no traffic 200 Alester Ave 2 miles gas station Valero road block nearby 899 Ames Ct 5 miles hospital Stanford Childrens Health moderate traffic 481 Amaranta Ave 1 miles parking garage Palo Alto Garage R moderate traffic Driver Address to the gas station. Dialogue Knowledge Base (KB) Car Valero is located at 200 Alester Ave. Car Since there is a road block nearby, I found another route for you and I sent it on your screen. Driver OK , please give me directions via a route that avoids all heavy traffic.