Recurrent Polynomial Network for Dialogue State Tracking

Dialogue state tracking ( DST ) is a process to estimate the distribution of the dialogue states as a dialogue progresses. Recent studies on constrained Markov Bayesian polynomial ( CMBP ) framework take the first step towards bridging the gap between rule-based and statistical approaches for DST . In this paper, the gap is further bridged by a novel framework -- recurrent polynomial network ( RPN ). RPN's unique structure enables the framework to have all the advantages of CMBP including efficiency, portability and interpretability . Additionally, RPN achieves more properties of statistical approaches than CMBP . RPN was evaluated on the data corpora of the second and the third Dialog State Tracking Challenge ( DSTC -2/3). Experiments showed that RPN can significantly outperform both traditional rule-based approaches and statistical approaches with similar feature set. Compared with the state-of-the-art statistical DST approaches with a lot richer features, RPN is also competitive.