Target-Based State and Tracking Algorithm for Spoken Dialogue System

Conventional spoken dialogue systems use frame structure to represent dialogue state. In this paper, we argue that using target distribution to represent dialogue state is much better than using frame structure. Based on the proposed target-based state, two target-based state tracking algorithms are introduced. Experiments in an end-to-end spoken dialogue system with real users are conducted to compare the performance between the target-based state trackers and frame-based state trackers. The experimental results show that the proposed target-based state tracker achieve 97% of dialogue success rate, comparing to 81% of frame-based state tracker, which suggests the advantage of target-based state.

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

[2]  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.

[3]  Milica Gasic,et al.  Gaussian Processes for Fast Policy Optimisation of POMDP-based Dialogue Managers , 2010, SIGDIAL Conference.

[4]  S. Young,et al.  Scaling POMDPs for Spoken Dialog Management , 2007, IEEE Transactions on Audio, Speech, and Language Processing.

[5]  Ji Wu,et al.  Subword scheme for keyword search , 2014, 2014 IEEE Spoken Language Technology Workshop (SLT).

[6]  政子 鶴岡,et al.  1998 IEEE International Conference on SMCに参加して , 1998 .

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

[8]  Xiaohui Zhang,et al.  Improving deep neural network acoustic models using generalized maxout networks , 2014, 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

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

[10]  Steve J. Young,et al.  Bayesian update of dialogue state for robust dialogue systems , 2008, 2008 IEEE International Conference on Acoustics, Speech and Signal Processing.

[11]  Wayne H. Ward,et al.  Recent Improvements in the CMU Spoken Language Understanding System , 1994, HLT.

[12]  Chin-Hui Lee,et al.  A Probabilistic Framework for Representing Dialog Systems and Entropy-Based Dialog Management Through Dynamic Stochastic State Evolution , 2015, IEEE/ACM Transactions on Audio, Speech, and Language Processing.

[13]  Jason D. Williams.  Incremental partition recombination for efficient tracking of multiple dialog states , 2010, 2010 IEEE International Conference on Acoustics, Speech and Signal Processing.

[14]  Ji Wu,et al.  An entropy minimization framework for goal-driven dialogue management , 2015, INTERSPEECH.