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

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.