Learning to Learn End-to-End Goal-Oriented Dialog From Related Dialog Tasks

Résumé fourni par la source

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.

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