CUED Publications database

Dialogue manager domain adaptation using Gaussian process reinforcement learning

Gasic, M and Mrksic, N and Rojas-Barahona, LM and Su, P-H and Ultes, S and Vandyke, D and Wen, T-H and Young, S (2017) Dialogue manager domain adaptation using Gaussian process reinforcement learning. Computer Speech and Language, 45. pp. 552-569. ISSN 0885-2308 (Unpublished)

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Spoken dialogue systems allow humans to interact with machines using natural speech. As such, they have many benefits. By using speech as the primary communication medium, a computer interface can facilitate swift, human-like acquisition of information. In recent years, speech interfaces have become ever more popular, as is evident from the rise of personal assistants such as Siri, Google Now, Cortana and Amazon Alexa. Recently, data-driven machine learning methods have been applied to dialogue modelling and the results achieved for limited-domain applications are comparable to or outperform traditional approaches. Methods based on Gaussian processes are particularly effective as they enable good models to be estimated from limited training data. Furthermore, they provide an explicit estimate of the uncertainty which is particularly useful for reinforcement learning. This article explores the additional steps that are necessary to extend these methods to model multiple dialogue domains. We show that Gaussian process reinforcement learning is an elegant framework that naturally supports a range of methods, including prior knowledge, Bayesian committee machines and multi-agent learning, for facilitating extensible and adaptable dialogue systems.

Item Type: Article
Uncontrolled Keywords: dialogue systems reinforcement learning Gaussian process
Divisions: Div F > Machine Intelligence
Depositing User: Cron Job
Date Deposited: 17 Jul 2017 19:19
Last Modified: 15 Apr 2021 06:11