NLUBroker: A Flexible and Responsive Broker for Cloud-based Natural Language Understanding Services

Authors: 

Lanyu Xu, Wayne State University; Arun Iyengar, IBM T.J. Watson Research Center; Weisong Shi, Wayne State University

Abstract: 

Cloud-based Natural Language Understanding (NLU) services are getting more and more popular with the development of artificial intelligence. More applications are integrated with cloud-based NLU services to enhance the way people communicate with machines. However, with NLU services provided by different companies powered by unrevealed AI technology, how to choose the best one is a problem for users. To our knowledge, there is currently no platform that can provide guidance to users and make recommendations based on their needs. To fill this gap, in this paper, we propose NLUBroker, a platform to comprehensively measure the performance indicators of candidate NLU services, and further provide a broker to select the most suitable service according to the different needs of users. Our evaluation shows that different NLU services leading in different aspects, and NLUBroker is able to improve the quality of experience by automatically choosing the best service. In addition, reinforcement learning is used to support NLUBroker by an intelligent agent in a dynamic environment, and the results are promising.

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BibTeX
@inproceedings {234853,
author = {Lanyu Xu and Arun Iyengar and Weisong Shi},
title = {NLUBroker: A Flexible and Responsive Broker for Cloud-based Natural Language Understanding Services},
booktitle = {11th {USENIX} Workshop on Hot Topics in Cloud Computing (HotCloud 19)},
year = {2019},
address = {Renton, WA},
url = {https://www.usenix.org/conference/hotcloud19/presentation/xu-lanyu},
publisher = {{USENIX} Association},
}