@inproceedings{williams2017demonstration,
author = {Williams, Jason and Liden, Lars},
title = {Demonstration of interactive dialog teaching for learning a practical end-to-end dialog manager},
booktitle = {Proceedings of 2017 SIGDIAL Conference},
year = {2017},
month = {August},
abstract = {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 distribution over
dialog actions. The benefit of this approach is that intermediate quantities such as intent or dialog state do not need to be labeled – rather, learning can be done directly on example dialogs.},
publisher = {Association for Computational Linguistics},
url = {http://approjects.co.za/?big=en-us/research/publication/demonstration-interactive-dialog-teaching-learning-practical-end-end-dialog-manager/},
edition = {Proceedings of 2017 SIGDIAL Conference},
}