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.

[1]  References , 1971 .

[2]  Milica Gasic,et al.  Effective handling of dialogue state in the hidden information state POMDP-based dialogue manager , 2011, TSLP.

[3]  Jason D. Williams.  Challenges and Opportunities for State Tracking in Statistical Spoken Dialog Systems: Results From Two Public Deployments , 2012, IEEE Journal of Selected Topics in Signal Processing.

[4]  Baining Guo,et al.  Spoken dialogue management as planning and acting under uncertainty , 2001, INTERSPEECH.

[5]  Matthew Henderson,et al.  The Second Dialog State Tracking Challenge , 2014, SIGDIAL Conference.

[6]  Matthew Henderson,et al.  The third Dialog State Tracking Challenge , 2014, 2014 IEEE Spoken Language Technology Workshop (SLT).

[7]  Lu Chen,et al.  The SJTU System for Dialog State Tracking Challenge 2 , 2014, SIGDIAL Conference.

[8]  Antoine Raux,et al.  The Dialog State Tracking Challenge , 2013, SIGDIAL Conference.

[9]  Jacek M. Zurada,et al.  Knowledge-based neurocomputing , 2000 .

[10]  Oliver Lemon,et al.  A Simple and Generic Belief Tracking Mechanism for the Dialog State Tracking Challenge: On the believability of observed information , 2013, SIGDIAL Conference.

[11]  Matthew Henderson,et al.  Word-Based Dialog State Tracking with Recurrent Neural Networks , 2014, SIGDIAL Conference.

[12]  J.D. Williams,et al.  Scaling up POMDPs for Dialog Management: The ``Summary POMDP'' Method , 2005, IEEE Workshop on Automatic Speech Recognition and Understanding, 2005..

[13]  F ROSENBLATT,et al.  The perceptron: a probabilistic model for information storage and organization in the brain. , 1958, Psychological review.

[14]  Filip Jurcícek,et al.  Comparison of Bayesian Discriminative and Generative Models for Dialogue State Tracking , 2013, SIGDIAL Conference.

[15]  Lu Chen,et al.  Constrained Markov Bayesian Polynomial for Efficient Dialogue State Tracking , 2015, IEEE/ACM Transactions on Audio, Speech, and Language Processing.

[16]  Geoffrey Zweig,et al.  Recent advances in deep learning for speech research at Microsoft , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.

[17]  Steve J. Young,et al.  Bayesian update of dialogue state: A POMDP framework for spoken dialogue systems , 2010, Comput. Speech Lang..

[18]  Miroslav Vodolán,et al.  Knowledge-based Dialog State Tracking , 2014, 2014 IEEE Spoken Language Technology Workshop (SLT).

[19]  Sungjin Lee,et al.  Structured Discriminative Model For Dialog State Tracking , 2013, SIGDIAL Conference.

[20]  Lu Chen,et al.  A generalized rule based tracker for dialogue state tracking , 2014, 2014 IEEE Spoken Language Technology Workshop (SLT).

[21]  Joelle Pineau,et al.  Spoken Dialogue Management Using Probabilistic Reasoning , 2000, ACL.

[22]  Maxine Eskénazi,et al.  Recipe For Building Robust Spoken Dialog State Trackers: Dialog State Tracking Challenge System Description , 2013, SIGDIAL Conference.

[23]  Milica Gasic,et al.  The Hidden Information State model: A practical framework for POMDP-based spoken dialogue management , 2010, Comput. Speech Lang..

[24]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[25]  Steve J. Young,et al.  Partially observable Markov decision processes for spoken dialog systems , 2007, Comput. Speech Lang..

[26]  Lu Chen,et al.  Semantic parser enhancement for dialogue domain extension with little data , 2014, 2014 IEEE Spoken Language Technology Workshop (SLT).

[27]  Jason D. Williams,et al.  Web-style ranking and SLU combination for dialog state tracking , 2014, SIGDIAL Conference.

[28]  Matthew Henderson,et al.  Robust dialog state tracking using delexicalised recurrent neural networks and unsupervised adaptation , 2014, 2014 IEEE Spoken Language Technology Workshop (SLT).