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Despite many recent advances for the design of dialogue systems, a truebottleneck remains the acquisition of data required to train its components.Unlike many other language processing applications, dialogue systems requireinteractions with users, therefore it is complex to develop them withpre-recorded data. Building on previous works, on-line learning is pursued hereas a most convenient way to address the issue. Data collection, annotation anduse in learning algorithms are performed in a single process. The maindifficulties are then: to bootstrap an initial basic system, and to control thelevel of additional cost on the user side. Considering that well-performingsolutions can be used directly off the shelf for speech recognition andsynthesis, the study is focused on learning the spoken language understandingand dialogue management modules only. Several variants of joint learning areinvestigated and tested with user trials to confirm that the overall on-linelearning can be obtained after only a few hundred training dialogues and canoverstep an expert-based system.

Joint On-line Learning of a Zero-shot Spoken Semantic Parser and a Reinforcement Learning Dialogue Manager