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.
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Titolo: | Deep Learning and Hierarchical Reinforcement Learning for modeling a Conversational Recommender System |
Autori: | |
Data di pubblicazione: | 2018 |
Rivista: | |
Handle: | http://hdl.handle.net/11586/232048 |
Appare nelle tipologie: | 1.1 Articolo in rivista |