A proposal for Developing and Deploying Statistical Dialog Management in Commercial Conversational Platforms

Cañas, Pablo; Griol Barres, David; Callejas Carrión, Zoraida

doi:https://doi.org/10.1007/978-3-031-15471-3_35

22___HAIS22___Preprint.pdf (425.9Kb)

Identificadores

URI: https://hdl.handle.net/10481/80260

DOI: https://doi.org/10.1007/978-3-031-15471-3_35

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Editorial

Springer

Materia

Conversational systems

Dialog management

Statistical approaches

DialogFlow

Fecha

2022

Referencia bibliográfica

This is a pre-print version of the chapter: Cañas, P., Griol, D., Callejas, Z. (2022). A Proposal for Developing and Deploying Statistical Dialog Management in Commercial Conversational Platforms. In: , et al. Hybrid Artificial Intelli- gent Systems. HAIS 2022. Lecture Notes in Computer Science(), vol 13469. Springer, Cham. https://doi.org/10. 1007/978-3-031-15471-3_35 (https://link.springer.com/chapter/10.1007/978-3-031-15471-3_35)

Resumen

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 corre- sponding 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