CUED Publications database

Inverse reinforcement learning for micro-turn management

Kim, D and Breslin, C and Tsiakoulis, P and Gašić, M and Henderson, M and Young, S (2014) Inverse reinforcement learning for micro-turn management. In: UNSPECIFIED pp. 328-332..

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Copyright © 2014 ISCA. Existing spoken dialogue systems are typically not designed to provide natural interaction since they impose a strict turn-taking regime in which a dialogue consists of interleaved system and user turns. To allow more responsive and natural interaction, this paper describes a system in which turn-taking decisions are taken at a more fine-grained micro-turn level. A decision-theoretic approach is then applied to optimise turntaking control. Inverse reinforcement learning is used to capture the complex but natural behaviours from human-human dialogues and optimise interaction without specifying a reward function manually. Using a corpus of human-human interaction, experiments show that IRL is able to learn an effective reward function which outperforms a comparable handcrafted policy.

Item Type: Conference or Workshop Item (UNSPECIFIED)
Divisions: Div F > Machine Intelligence
Depositing User: Cron Job
Date Deposited: 17 Jul 2017 19:40
Last Modified: 22 May 2018 07:18