Paper Info
Title
Hybrid Dialog State Tracker
Abstract
This paper presents a hybrid dialog state tracker that combines a rule based and a machine learning based approach to belief state tracking. Therefore, we call it a hybrid tracker. The machine learning in our tracker is realized by a Long Short Term Memory (LSTM) network. To our knowledge, our hybrid tracker sets a new state-of-the-art result for the Dialog State Tracking Challenge (DSTC) 2 dataset when the system uses only live SLU as its input.
Year
Venue
Field
2015
CoRR
Dialog box,Rule-based system,Computer science,Long short term memory,Artificial intelligence,Machine learning
DocType
Volume
Citations 
Journal
abs/1510.03710
2
PageRank 
References 
Authors
0.38
15
3
Name
Order
Citations
PageRank
Miroslav Vodolán131.08
Rudolf Kadlec222916.25
Jan Kleindienst322023.74