In this paper, we propose a framework based on Hierarchical Reinforcement Learning for dialogue management in a Conversational Recommender System scenario. The framework splits the dialogue into more manageable tasks whose achievement corresponds to goals of the dialogue with the user. The framework consists of a meta-controller, which receives the user utterance and understands which goal should pursue, and a controller, which exploits a goal-specific representation to generate an answer composed by a sequence of tokens. The modules are trained using a two-stage strategy based on a preliminary Supervised Learning stage and a successive Reinforcement Learning stage.
CITATION STYLE
Greco, C., Suglia, A., Basile, P., & Semeraro, G. (2017). Converse-et-impera: Exploiting deep learning and hierarchical reinforcement learning for conversational recommender systems. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10640 LNAI, pp. 372–386). Springer Verlag. https://doi.org/10.1007/978-3-319-70169-1_28
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