Machine Learning for Dialog State Tracking: A Review
Venue
Proceedings of The First International Workshop on Machine Learning in Spoken Language Processing (2015)
Publication Year
2015
Authors
BibTeX
Abstract
Spoken dialog systems help users achieve a task using natural language. Noisy
speech recognition and ambiguity in natural language motivate statistical
approaches that exploit distributions over the user's goal at every step in the
dialog. The task of tracking these distributions, termed Dialog State Tracking, is
therefore an essential component of any Spoken dialog system. In recent years, the
Dialog State Tracking Challenges have provided a common test-bed and evaluation
framework for this task, as well as labeled dialog data. As a result, a variety of
machine-learned methods have been successfully applied to Dialog State Tracking.
This paper reviews the machine-learning techniques that have been adapted to Dialog
State Tracking, and gives an overview of published evaluations. Discriminative
machine-learned methods outperform generative and rule-based methods, the previous
state-of-the-art.
