A Network-based End-to-End Trainable Task-oriented Dialogue System

Understand

Teaching machines to accomplish tasks by conversing naturally with humans is challenging.

• Currently, developing task-oriented dialogue systems requires creating multiple components and typically this involves either a large amount of handcrafting, or acquiring costly labelled datasets to solve a statistical learning problem for each component.
• In this work we introduce a neural network-based text-in, text-out end-to-end trainable goal-oriented dialogue system along with a new way of collecting dialogue data based on a novel pipe-lined Wizard-of-Oz framework.
• This approach allows us to develop dialogue systems easily and without making too many assumptions about the task at hand.