Implementation of a Statistical Dialogue Manager for Commercial Conversational Systems

Cañas, Pablo; Griol Barres, David

doi:https://doi.org/10.1007/978-3-030-57802-2_37

Soco20-CanasGriol.pdf (296.4Ko)

Identificadores

URI: https://hdl.handle.net/10481/80253

DOI: https://doi.org/10.1007/978-3-030-57802-2_37

Exportar

Editorial

Springer

Materia

Conversational systems

Dialogue management

Machine learning

DialogFlow

Date

2021

Referencia bibliográfica

Published version: Cañas, P., Griol, D. (2021). Implementation of a Statistical Dialogue Manager for Commercial Conversational Systems. In: Herrero, Á., Cambra, C., Urda, D., Sedano, J., Quintián, H., Corchado, E. (eds) 15th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2020). SOCO 2020. Advances in Intelligent Systems and Computing, vol 1268. Springer, Cham. [https://doi.org/10.1007/978-3-030-57802-2_37]

Patrocinador

European Union’s Horizon 2020 research and innovation programme under grant agreement No 823907

Résumé

Conversational interfaces have recently become an ubiquitous element in both the personal sphere by improving individual’s quality of life, and industrial environments by the automation of services and its corresponding costs savings. However, designing the dialogue model used by these interfaces to decide the next response is a hard-to-accomplish task for complex conversational interactions. In this paper, we propose a statistical-based dialogue manager architecture, which provides flexibility to develop and maintain this module. Our proposal has been integrated with DialogFlow, a natural language understanding platform provided by Google to design conversational user interfaces. The proposed architecture has been assessed with a real use case for a train scheduling domain, proving that the user experience is of a high value and it can be integrated for commercial setups.