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User-aware dialogue management policies over attributed bi-automata

  • Manex Serras
  • María Inés Torres
  • Arantza del Pozo
Original Article

Abstract

Designing dialogue policies that take user behavior into account is complicated due to user variability and behavioral uncertainty. Attributed probabilistic finite-state bi-automata (A-PFSBA) have proven to be a promising framework to develop dialogue managers that capture the users’ actions in its structure and adapt to them online, yet developing policies robust to high user uncertainty is still challenging. In this paper, the theoretical A-PFSBA dialogue management framework is augmented by formally defining the notation of exploitation policies over its structure. Under such definition, multiple path-based policies are implemented, those that take into account external information and those which do not. These policies are evaluated on the Let’s Go corpus, before and after an online learning process whose goal is to update the initial model through the interaction with end users. In these experiments the impact of user uncertainty and the model structural learning is thoroughly analyzed.

Keywords

Dialogue systems User adaptation Attributed bi-automata Dialogue management Path-based policies 

Notes

Acknowledgements

This work has been partially founded by the Spanish Minister of Science under Grants TIN2014-54288-C4-4-R and TIN2017-85854-C4-3-R and by the European Commission H2020 SC1-PM15 EMPATHIC project, RIA Grant 69872.

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Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2018

Authors and Affiliations

  1. 1.Speech and Natural Language Technologies, VicomtechDonostia/San SebastiánSpain
  2. 2.Speech Interactive Research GroupUniversidad del País Vasco, UPV/EHULeioaSpain

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