Abstract
Conversational interfaces have recently become ubiquitous in the personal sphere by improving an individual’s quality of life and industrial environments by automating services and their corresponding cost savings. However, designing the dialog model used by these interfaces to decide the following response is a hard-to-accomplish task for complex conversational interactions. This paper proposes a statistical-based dialog manager architecture, which provides flexibility to develop and maintain this module. Our proposal has been integrated using DialogFlow, a natural language understanding platform provided by Google to design conversational user interfaces. The proposed hybrid architecture has been assessed with a real use case for a train scheduling domain, proving that the user experience is highly valued and can be integrated into commercial setups.
The research leading to these results has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement no. 823907 (MENHIR project: https://menhir-project.eu) and the projects supported by the Spanish Ministry of Science and Innovation through the GOMINOLA (PID2020-118112RB-C21 and PID2020-118112RB-C22, funded by MCIN/AEI/10.13039/501100011033), and CAVIAR (TEC2017-84593-C2-1-R, funded by MCIN/AEI/10.13039/501100011033/FEDER).
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Cañas, P., Griol, D., Callejas, Z. (2022). A Proposal for Developing and Deploying Statistical Dialog Management in Commercial Conversational Platforms. In: García Bringas, P., et al. Hybrid Artificial Intelligent Systems. HAIS 2022. Lecture Notes in Computer Science(), vol 13469. Springer, Cham. https://doi.org/10.1007/978-3-031-15471-3_35
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