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National Repository of Grey Literature 1 records found  Search took 0.01 seconds. 
Practical neural dialogue management using pretrained language models
 Šafář, Jaroslav ; Dušek, Ondřej (advisor) ; Bojar, Ondřej (referee)
Task-oriented dialogue systems pose a significant challenge due to their complexity and the need to handle components such as language understanding, state tracking, action selection, and language generation. In this work, we explore the improvements in dialogue management using pretrained language models. We propose three models that incorporate pretrained language models, aiming to provide a practical approach to designing dialogue systems capable of effectively addressing the language understanding, state tracking, and action selection tasks. Our dialogue state tracking model achieves a joint goal accuracy of 74%. We also identify limitations in handling complex or multi- step user requests in the action selection task. This research underscores the potential of pretrained language models in dialogue management while highlighting areas for further improvement. 1
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