@inproceedings{williams2015rapidly,
author = {Williams, Jason and Niraula, Nobal B. and Dasigi, Pradeep and Lakshmiratan, Aparna and Garcia Jurado Suarez, Carlos and Reddy, Mouni and Zweig, Geoffrey},
title = {Rapidly Scaling Dialog Systems with Interactive Learning},
year = {2015},
month = {January},
abstract = {In personal assistant dialog systems, intent models are classifiers that identify the intent of a user utterance, such as to add a meeting to a calendar, or get the director of a stated movie. Rapidly adding intents is one of the main bottlenecks to scaling — adding functionality to — personal assistants. In this paper we show how interactive learning can be applied to the creation of statistical intent models. Interactive learning [10] combines model definition, labeling, model building, active learning, model evaluation, and feature engineering in a way that allows a domain expert — who need not be a machine learning expert — to build classifiers. We apply interactive learning to build a handful of intent models in three different domains. In controlled lab experiments, we show that intent detectors can be built using interactive learning, and then improved in a novel end-to-end visualization tool. We then applied this method to a publicly deployed personal assistant — Microsoft Cortana — where a non-machine learning expert built an intent model in just over two hours, yielding excellent performance in the commercial service.},
url = {http://approjects.co.za/?big=en-us/research/publication/rapidly-scaling-dialog-systems-with-interactive-learning/},
}