In this paper, reinforcement learning (RL) is used to learn an efficient turn-taking management model in a simulated slot-filling task with the objective of minimising the dialogue duration and maximising the completion task ratio. Turn-taking decisions are handled in a separate new module, the Scheduler. Unlike most dialogue systems, a dialogue turn is split into micro-turns and the Scheduler makes a decision for each one of them. A Fitted Value Iteration algorithm, Fitted-Q, with a linear state representation is used for learning the state to action policy. Comparison between a non-incremental and an incremental handcrafted strategies, taken as baselines, and an incremental RL-based strategy, shows the latter to be significantly more efficient, especially in noisy environments.<\/p>\n","protected":false},"excerpt":{"rendered":"
In this paper, reinforcement learning (RL) is used to learn an efficient turn-taking management model in a simulated slot-filling task with the objective of minimising the dialogue duration and maximising the completion task ratio. Turn-taking decisions are handled in a separate new module, the Scheduler. Unlike most dialogue systems, a dialogue turn is split into […]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"footnotes":""},"msr-content-type":[3],"msr-research-highlight":[],"research-area":[13556],"msr-publication-type":[193716],"msr-product-type":[],"msr-focus-area":[],"msr-platform":[],"msr-download-source":[],"msr-locale":[268875],"msr-field-of-study":[],"msr-conference":[],"msr-journal":[],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-401885","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-artificial-intelligence","msr-locale-en_us"],"msr_publishername":"","msr_edition":"Proceedings of the 16th Annual Meeting of the Special Interest Group on Discourse and Dialogue 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