Recurrent Polynomial Network for Dialogue State Tracking

Authors

  • Kai Sun Department of Computer Science, Cornell University
  • Qizhe Xie Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering
  • Kai Yu Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering

DOI:

https://doi.org/10.5087/dad.2016.303

Abstract

  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.

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Published

2016-04-15

Issue

Section

Articles