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Safe transfer learning for dialogue applications

Nicolas Carrara 1, 2 Romain Laroche 3 Jean-Léon Bouraoui 1 Tanguy Urvoy 1 Olivier Pietquin 4
2 SEQUEL - Sequential Learning
Inria Lille - Nord Europe, CRIStAL - Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189
Abstract : In this paper, we formulate the hypothesis that the first dialogues with a new user should be handle in a very conservative way, for two reasons : avoid user dropout; gather more successful dialogues to speedup the learning of the asymptotic strategy. To this extend, we propose to transfer a safe strategy to initiate the first dialogues.
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Submitted on : Tuesday, November 20, 2018 - 1:46:18 PM
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Nicolas Carrara, Romain Laroche, Jean-Léon Bouraoui, Tanguy Urvoy, Olivier Pietquin. Safe transfer learning for dialogue applications. SLSP 2018 - 6th International Conference on Statistical Language and Speech Processing, Oct 2018, Mons, Belgium. ⟨hal-01928102⟩



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