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Statistical Dialog Tracking and Management for Task-Oriented Conversational Systems

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Hybrid Artificial Intelligent Systems (HAIS 2023)

Abstract

Conversational interfaces offer users a natural way to interact with a range of applications and devices. Human-machine interaction using these systems involves different components that mimic the mechanisms used by humans when using language and speech interaction. In this paper, we are interested in automatically developing the dialog state tracking component for task-oriented conversational systems to automatically decide the best system response from a set of predefined responses. To do this, we have evaluated several statistical methodologies to develop this component for a conversational system that provides technical program information during conferences. Gradient boosting trees have been selected as the best performing method for this specific domain.

The research leading to these results has received funding from “CONVERSA: Effective and efficient resources and models for transformative conversational AI in Spanish and co-official languages” project with reference TED2021-132470B-I00, funded by MCIN/AEI/10.13039/501100011033 and by the European Union “NextGenerationEU”/PRTR”. Work also partially supported from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 823907 (MENHIR project: https://menhir-project.eu) and the GOMINOLA project (PID2020-118112RB-C21 and PID2020-118112RB-C22, funded by MCIN/AEI/10.13039/501100011033).

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Correspondence to David Griol .

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Griol, D., Callejas, Z. (2023). Statistical Dialog Tracking and Management for Task-Oriented Conversational Systems. In: García Bringas, P., et al. Hybrid Artificial Intelligent Systems. HAIS 2023. Lecture Notes in Computer Science(), vol 14001. Springer, Cham. https://doi.org/10.1007/978-3-031-40725-3_43

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  • DOI: https://doi.org/10.1007/978-3-031-40725-3_43

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  • Publisher Name: Springer, Cham

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