Goal-conditioned User Modeling for Dialogue Systems using Stochastic Bi-Automata

Manex Serras, María Inés Torres, Arantza del Pozo

Abstract

User Models (UM) are commonly employed to train and evaluate dialogue systems as they generate dialogue samples that simulate end-user behavior. This paper presents a stochastic approach for user modeling based in Attributed Probabilistic Finite State Bi-Automata (A-PFSBA). This framework allows the user model to be conditioned by the dialogue goal in task-oriented dialogue scenarios. In addition, the work proposes two novel smoothing policies that employ the K-nearest A-PFSBA states to infer the next UM action in unseen interactions. Experiments on the Dialogue State Tracking Challenge 2 (DSTC2) corpus provide results similar to the ones obtained through deep learning based user modeling approaches in terms of F1 measure. However the proposed Bi-Automata User Model (BAUM) requires less resources both of memory and computing time.

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Paper Citation


in Harvard Style

Serras M., Torres M. and del Pozo A. (2019). Goal-conditioned User Modeling for Dialogue Systems using Stochastic Bi-Automata.In Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-351-3, pages 128-134. DOI: 10.5220/0007359401280134


in Bibtex Style

@conference{icpram19,
author={Manex Serras and María Inés Torres and Arantza del Pozo},
title={Goal-conditioned User Modeling for Dialogue Systems using Stochastic Bi-Automata},
booktitle={Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2019},
pages={128-134},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007359401280134},
isbn={978-989-758-351-3},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Goal-conditioned User Modeling for Dialogue Systems using Stochastic Bi-Automata
SN - 978-989-758-351-3
AU - Serras M.
AU - Torres M.
AU - del Pozo A.
PY - 2019
SP - 128
EP - 134
DO - 10.5220/0007359401280134