SIM: A Slot-Independent Neural Model for Dialogue State Tracking

Dialogue state tracking is an important component in task-oriented dialogue systems to identify users' goals and requests as a dialogue proceeds. However, as most previous models are dependent on dialogue slots, the model complexity soars when the number of slots increases. In this paper, we put forward a slot-independent neural model (SIM) to track dialogue states while keeping the model complexity invariant to the number of dialogue slots. The model utilizes attention mechanisms between user utterance and system actions. SIM achieves state-of-the-art results on WoZ and DSTC2 tasks, with only 20% of the model size of previous models.

[1]  Filip Jurcícek,et al.  Incremental LSTM-based dialog state tracker , 2015, 2015 IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU).

[2]  Dilek Z. Hakkani-Tür,et al.  Scalable multi-domain dialogue state tracking , 2017, 2017 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU).

[3]  Jeffrey Pennington,et al.  GloVe: Global Vectors for Word Representation , 2014, EMNLP.

[4]  Dilek Z. Hakkani-Tür,et al.  Dialogue Learning with Human Teaching and Feedback in End-to-End Trainable Task-Oriented Dialogue Systems , 2018, NAACL.

[5]  Ariel D. Procaccia,et al.  Variational Dropout and the Local Reparameterization Trick , 2015, NIPS.

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

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

[8]  Tsung-Hsien Wen,et al.  Neural Belief Tracker: Data-Driven Dialogue State Tracking , 2016, ACL.

[9]  Richard Socher,et al.  Global-Locally Self-Attentive Encoder for Dialogue State Tracking , 2018, ACL.

[10]  David Vandyke,et al.  A Network-based End-to-End Trainable Task-oriented Dialogue System , 2016, EACL.

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

[12]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[13]  Pawel Budzianowski,et al.  Large-Scale Multi-Domain Belief Tracking with Knowledge Sharing , 2018, ACL.

[14]  Ivan Vulić,et al.  Fully Statistical Neural Belief Tracking , 2018, ACL.