Demonstration of interactive teaching for end-to-end dialog control with hybrid code networks
J. D. Williams, Lars Lidén · 2017
This is a demonstration of interactive teaching for practical end-to-end dialog systems driven by a recurrent neural network.In this approach, a developer teaches the network by interacting with the system and providing on-the-spot corrections.Once a system is deployed, a developer can also correct mistakes in logged dialogs.This demonstration shows both of these teaching methods applied to dialog systems in three domains: pizza ordering, restaurant information, and weather forecasts.