A Novel Dialogue Manager Model for Spoken Dialogue Systems Based on User Input Learning

@InProceedings{shariff19_dialog_manager,
   author="Ahmed Shariff, M. F.
   and Nawarathna, Ruwan D.",
   editor="Hemanth, Jude
   and Silva, Thushari
   and Karunananda, Asoka",
   title="A Novel Dialogue Manager Model for Spoken Dialogue Systems Based on User Input Learning",
   booktitle="Artificial Intelligence",
   year="2019",
   publisher="Springer Singapore",
   address="Singapore",
   pages="187--199",
   abstract="The complexity of the dialogue manager is a major issue in spoken dialogue systems. In this work, a novel dialogue manager based on user input learning is proposed to overcome this issue. In the proposed model back-end functionality is considered as a set of functions a user can trigger through the dialogue manager. It uses these functions as classes for the classification of user inputs. To maintain the context of the dialogue interactions, a context tree is used. Consequently, the model performs its task as two classification tasks to identify the function a user input may trigger and use the context to maintain the discourse of the dialogue. The model shows promising results and proves that a dialogue manager can be integrated into a spoken dialogue system much more directly with less hassle.",
   isbn="978-981-13-9129-3"
}