Web-style ranking and SLU combination for 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.

Download Full-text

Dialog state tracking, a machine reading approach using Memory Network

10.18653/v1/e17-1029 ◽

2017 ◽

Cited By ~ 2

Author(s):

Julien Perez ◽

Fei Liu

Keyword(s):

Memory Network ◽

State Tracking ◽

Dialog State Tracking ◽

Machine Reading

Download Full-text

Spectral decomposition method of dialog state tracking via collective matrix factorization

Dialogue & Discourse ◽

10.5087/dad.2016.304 ◽

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.

Download Full-text