This is a demonstration of a platform for building practical, task-oriented, end-to-end dialog systems. Whereas traditional dialog systems consists of a pipeline of components such as intent detection,
\nstate tracking, and action selection, an end-to-end dialog system is driven by a machine learning model which takes observable dialog history as input, and directly outputs a distribution over
\ndialog actions. The benefit of this approach is that intermediate quantities such as intent or dialog state do not need to be labeled \u2013 rather, learning can be done directly on example dialogs.<\/p>\n","protected":false},"excerpt":{"rendered":"
This is a demonstration of a platform for building practical, task-oriented, end-to-end dialog systems. Whereas traditional dialog systems consists of a pipeline of components such as intent detection, state tracking, and action selection, an end-to-end dialog system is driven by a machine learning model which takes observable dialog history as input, and directly outputs a 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