Implementation of a Statistical Dialogue Manager for Commercial Conversational Systems Cañas, Pablo Griol Barres, David Conversational systems Dialogue management Machine learning DialogFlow 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. 2023-02-27T08:34:36Z 2023-02-27T08:34:36Z 2021 info:eu-repo/semantics/conferenceObject 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] https://hdl.handle.net/10481/80253 https://doi.org/10.1007/978-3-030-57802-2_37 eng info:eu-repo/grantAgreement/EC/H2020/823907 info:eu-repo/semantics/openAccess Springer