The Fourth Dialog State Tracking Challenge

2016 ◽  
pp. 435-449 ◽  
Author(s):  
Seokhwan Kim ◽  
Luis Fernando D’Haro ◽  
Rafael E. Banchs ◽  
Jason D. Williams ◽  
Matthew Henderson
Keyword(s):  
State Tracking ◽  
Dialog State Tracking

Knowledge-based Dialog State Tracking

2014 ◽  
Author(s):  
Rudolf Kadlec ◽  
Miroslav Vodolan ◽  
Jindrich Libovicky ◽  
Jan Macek ◽  
Jan Kleindienst
Keyword(s):  
Knowledge Based ◽  
State Tracking ◽  
Dialog State Tracking

Dialog state tracking with attention-based sequence-to-sequence learning

2016 ◽  
Author(s):  
Takaaki Hori ◽  
Hai Wang ◽  
Chiori Hori ◽  
Shinji Watanabe ◽  
Bret Harsham ◽  
...  
Keyword(s):  
Sequence Learning ◽  
State Tracking ◽  
Dialog State Tracking

scholarly journals The Dialog State Tracking Challenge Series

AI Magazine ◽  
2014 ◽  
Vol 35 (4) ◽  
pp. 121-124 ◽  
Author(s):  
Jason D. Williams ◽  
Matthew Henderson ◽  
Antoine Raux ◽  
Blaise Thomson ◽  
Alan Black ◽  
...  
Keyword(s):  
Research Community ◽  
Spoken Dialog Systems ◽  
Dialog Systems ◽  
New Methods ◽  
State Tracking ◽  
Dialog State Tracking

In spoken dialog systems, dialog state tracking refers to the task of correctly inferring the user's goal at a given turn, given all of the dialog history up to that turn. The Dialog State Tracking Challenge is a research community challenge task that has run for three rounds. The challenge has given rise to a host of new methods for dialog state tracking, and also deeper understandings about the problem itself, including methods for evaluation.

scholarly journals Spectral decomposition method of dialog state tracking via collective matrix factorization

2016 ◽  
Vol 7 (3) ◽  
pp. 34-46
Author(s):  
Julien Perez
Keyword(s):  
Matrix Factorization ◽  
The State ◽  
Computationally Efficient ◽  
Reward Function ◽  
Dialog Management ◽  
Dialog System ◽  
Dependent Variables ◽  
Novel Method ◽  
State Tracking ◽  
Dialog State Tracking

The task of dialog management is commonly decomposed into two sequential subtasks: dialog state tracking and dialog policy learning. In an end-to-end dialog system, the aim of dialog state tracking is to accurately estimate the true dialog state from noisy observations produced by the speech recognition and the natural language understanding modules. The state tracking task is primarily meant to support a dialog policy. From a probabilistic perspective, this is achieved by maintaining a posterior distribution over hidden dialog states composed of a set of context dependent variables. Once a dialog policy is learned, it strives to select an optimal dialog act given the estimated dialog state and a defined reward function. This paper introduces a novel method of dialog state tracking based on a bilinear algebric decomposition model that provides an efficient inference schema through collective matrix factorization. We evaluate the proposed approach on the second Dialog State Tracking Challenge (DSTC-2) dataset and we show that the proposed tracker gives encouraging results compared to the state-of-the-art trackers that participated in this standard benchmark. Finally, we show that the prediction schema is computationally efficient in comparison to the previous approaches.

scholarly journals The Second Dialog State Tracking Challenge

2014 ◽  
Author(s):  
Matthew Henderson ◽  
Blaise Thomson ◽  
Jason D Williams
Keyword(s):  
State Tracking ◽  
Dialog State Tracking

scholarly journals How to Tame Your Data: Data Augmentation for Dialog State Tracking

2020 ◽  
Author(s):  
Adam Summerville ◽  
Jordan Hashemi ◽  
James Ryan ◽  
william ferguson
Keyword(s):  
Data Augmentation ◽  
State Tracking ◽  
Dialog State Tracking
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