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Date of Award
Cloud-based Natural Language Understanding (NLU) services are becoming 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 developers. Existing tools which can provide guidance to developers and make recommendations based on their needs are severely limited. In this paper, we comprehensively evaluate multiple state-of-the-art NLU services, and the results indicate that there is no absolute winner for diﬀerent usage requirements. Motivated by this observation, we discuss ﬁve insights and propose NLUBroker, a QoE-driven broker system to select the proper service according to the environment. NLUBroker senses the client and service status and leverages a solution to the multi-armed bandit problem to conduct online learning, aiming to achieve maximum expected QoE. The performance of NLUBroker is evaluated in both simulation and real-world environments, and the evaluation results demonstrate that NLUBroker is an eﬃcient solution for selecting NLU services. It is adaptive to changes in the environment, outperforms three baseline methods we evaluated and improves overall QoE up to 1.5X for the evaluated state-of-the-art NLU services.
Xu, Lanyu, "Nlubroker: A Qoe-Driven Evaluation And Broker System For Natural Language Understanding Services" (2020). Wayne State University Theses. 800.