Inverse reinforcement learning for micro-turn management
Dongho Kim, Catherine J. Breslin, Pirros Tsiakoulis, Milomir M. Gašić, Matthew Henderson, Steve J. Young · 2014
Existing spoken dialogue systems are typically not de-signed 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 turn-taking control. Inverse reinforcement learning is used to cap-ture the complex but natural behaviours from human-human di-alogues and optimise interaction without specifying a reward function manually. Using a corpus of human-human interac-tion, experiments show that IRL is able to learn an effective reward function which outperforms a comparable handcrafted policy. Index Terms: dialogue management, spoken dialogue systems, inverse reinforcement learning, Markov decision processes