Learning to Learn End-to-End Goal-Oriented Dialog From Related Dialog Tasks
This addresses the costly data collection issue for dialog systems, though it is incremental as it builds on existing meta-learning and transfer learning approaches.
The paper tackles the problem of data scarcity for end-to-end goal-oriented dialog systems by using meta-learning to selectively incorporate data from related dialog tasks, achieving significant accuracy improvements in an example task.
For each goal-oriented dialog task of interest, large amounts of data need to be collected for end-to-end learning of a neural dialog system. Collecting that data is a costly and time-consuming process. Instead, we show that we can use only a small amount of data, supplemented with data from a related dialog task. Naively learning from related data fails to improve performance as the related data can be inconsistent with the target task. We describe a meta-learning based method that selectively learns from the related dialog task data. Our approach leads to significant accuracy improvements in an example dialog task.