Article Structure
Abstract
This paper proposes a novel user intention simulation method which is a data-driven approach but able to integrate diverse user discourse knowledge together to simulate various type of users.
Introduction
User simulation techniques are widely used for learning optimal dialog strategies in a statistical dialog management framework and for automated evaluation of spoken dialog systems.
Related work
Data-driven intention modeling approach uses statistical methods to generate the user intention given discourse information (history).
Overall architecture
The overall architecture of our user simulator is shown in Fig.
Topics
logistic regression
- In Markov logic framework, logistic regression based data-driven user intention modeling is introduced, and human dialog knowledge are designed into two layers such as domain and discourse knowledge, then it is integrated with the data-driven model in generation time.Page 1, “Abstract”
- The formulas for user intention modeling based on logistic regressionPage 3, “Overall architecture”
- A logistic regression model is used for the statistical user intention model in Markov logic.Page 4, “Overall architecture”
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n-gram
- N-gram based approaches (Eckert et al., 1997, Levin et al., 2000) and other approaches (Scheffler and Young, 2001, Pietquin and Dutoit, 2006, Schatzmann et al., 2007) are introduced.Page 1, “Related work”
- The unseen rate of n-gram varies according to the simulated user.Page 4, “Overall architecture”
- Notice that simulated user C, E and H generates higher unseen n-gram patterns over all word error settings.Page 4, “Overall architecture”
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