Policy optimization of dialogue management in spoken dialogue system for out-of-domain utterances

2016 ◽  
Author(s):  
Yuhong Xu ◽  
Peijie Huang ◽  
Jiecong Tang ◽  
Qiangjia Huang ◽  
Zhenpeng Deng ◽  
...  
Keyword(s):  
Dialogue System ◽  
Spoken Dialogue ◽  
Spoken Dialogue System ◽  
Dialogue Management ◽  
Policy Optimization

scholarly journals Optimizing Dialogue Management with Reinforcement Learning: Experiments with the NJFun System

2002 ◽  
Vol 16 ◽  
pp. 105-133 ◽  
Author(s):  
S. Singh ◽  
D. Litman ◽  
M. Kearns ◽  
M. Walker
Keyword(s):  
Reinforcement Learning ◽  
System Performance ◽  
Empirical Evaluation ◽  
Learning Approach ◽  
Access To Information ◽  
Dialogue System ◽  
Spoken Dialogue ◽  
Spoken Dialogue System ◽  
Dialogue Management ◽  
Design Construction

Designing the dialogue policy of a spoken dialogue system involves many nontrivial choices. This paper presents a reinforcement learning approach for automatically optimizing a dialogue policy, which addresses the technical challenges in applying reinforcement learning to a working dialogue system with human users. We report on the design, construction and empirical evaluation of NJFun, an experimental spoken dialogue system that provides users with access to information about fun things to do in New Jersey. Our results show that by optimizing its performance via reinforcement learning, NJFun measurably improves system performance.

scholarly journals An Interactive Dialogue Management System for Spoken Language

2021 ◽  
Vol 3 (27) ◽  
pp. 82-100
Author(s):  
Manal Alqahtani ◽  
Keyword(s):  
Management System ◽  
Survey Design ◽  
Main Question ◽  
Mixed Method Research ◽  
Dialogue System ◽  
Important Conclusion ◽  
Spoken Dialogue ◽  
Spoken Dialogue System ◽  
Dialogue Management ◽  
Machine Communication

The spoken dialogue system is one of the most important human-machine communication ways. Human-machine communication can be described as an interaction between the user and the computer. This field is full of research points, so it is considered a good attractive environment for many researchers. The spoken dialogue system is of great importance in the process of communicating commercial applications, and facilitating the connecting process between the human and machine which may take different faces. The main objective of this research will be building an interactive dialogue management system for spoken dialogue system in an ideal way, By answering the following main question: How can we build an interactive dialogue management system for spoken dialogue system in an ideal way has the ability to accomplish the Naturalness, Usability, Mixed initiative, Co-operativity, Robustness, and Exploration. This research will be a mixed-method research and will adopt a descriptive survey design in collecting information by Survey questionnaires, Interviews to a sample of the target population, and while secondary data will be found from books, journals, and The Internet. The most important conclusion of the research is the spoken dialogue system is to be less complexity and use uncertainty model; this way must be acceptable by the user and the system itself.

Speech understanding, dialogue management and response generation in corpus-based spoken dialogue system

2004 ◽  
Author(s):  
Keita Hayashi ◽  
Yuki Irie ◽  
Yukiko Yamaguchi ◽  
Shigeki Matsubara ◽  
Nobuo Kawaguchi
Keyword(s):  
Speech Understanding ◽  
Dialogue System ◽  
Spoken Dialogue ◽  
Spoken Dialogue System ◽  
Dialogue Management

scholarly journals Combining POMDPs trained with user simulations and rule-based dialogue management in a spoken dialogue system

2009 ◽  
Author(s):  
Sebastian Varges ◽  
Silvia Quarteroni ◽  
Giuseppe Riccardi ◽  
Alexei V. Ivanov ◽  
Pierluigi Roberti
Keyword(s):  
Dialogue System ◽  
Spoken Dialogue ◽  
Spoken Dialogue System ◽  
Rule Based ◽  
Dialogue Management

scholarly journals Characterizing and Predicting Corrections in Spoken Dialogue Systems

2006 ◽  
Vol 32 (3) ◽  
pp. 417-438 ◽  
Author(s):  
Diane Litman ◽  
Julia Hirschberg ◽  
Marc Swerts
Keyword(s):  
Speech Recognition ◽  
Predictive Power ◽  
Classification Error ◽  
Dialogue Systems ◽  
Dialogue System ◽  
Spoken Dialogue Systems ◽  
Experimental Conditions ◽  
Spoken Dialogue ◽  
Spoken Dialogue System ◽  
Recognition Errors

This article focuses on the analysis and prediction of corrections, defined as turns where a user tries to correct a prior error made by a spoken dialogue system. We describe our labeling procedure of various corrections types and statistical analyses of their features in a corpus collected from a train information spoken dialogue system. We then present results of machine-learning experiments designed to identify user corrections of speech recognition errors. We investigate the predictive power of features automatically computable from the prosody of the turn, the speech recognition process, experimental conditions, and the dialogue history. Our best-performing features reduce classification error from baselines of 25.70–28.99% to 15.72%.

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