  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.
