Sequence to Sequence Modeling for User Simulation in Dialog Systems

2017 ◽  
Author(s):  
Paul Crook ◽  
Alex Marin
Keyword(s):  
Dialog Systems ◽  
Sequence Modeling ◽  
User Simulation

Assessing user simulation for dialog systems using human judges and automatic evaluation measures

2011 ◽  
Vol 17 (4) ◽  
pp. 511-540 ◽  
Author(s):  
HUA AI ◽  
DIANE LITMAN
Keyword(s):  
Gold Standard ◽  
System Development ◽  
Automatic Evaluation ◽  
Dialog Systems ◽  
Evaluation Measures ◽  
Assessment Study ◽  
User Simulation ◽  
Dialog System ◽  
Ranking Model

AbstractWhile different user simulations are built to assist dialog system development, there is an increasing need to quickly assess the quality of the user simulations reliably. Previous studies have proposed several automatic evaluation measures for this purpose. However, the validity of these evaluation measures has not been fully proven. We present an assessment study in which human judgments are collected on user simulation qualities as the gold standard to validate automatic evaluation measures. We show that a ranking model can be built using the automatic measures to predict the rankings of the simulations in the same order as the human judgments. We further show that the ranking model can be improved by using a simple feature that utilizes time-series analysis.

Data-driven user simulation for automated evaluation of spoken dialog systems

2009 ◽  
Vol 23 (4) ◽  
pp. 479-509 ◽  
Author(s):  
Sangkeun Jung ◽  
Cheongjae Lee ◽  
Kyungduk Kim ◽  
Minwoo Jeong ◽  
Gary Geunbae Lee
Keyword(s):  
Data Driven ◽  
Spoken Dialog Systems ◽  
Dialog Systems ◽  
Automated Evaluation ◽  
User Simulation

Spoken Natural Language Dialog Systems

1995 ◽  
Author(s):  
Ronnie W. Smith ◽  
D. Richard Hipp
Keyword(s):  
Problem Solving ◽  
Natural Language ◽  
Error Correction ◽  
User Model ◽  
Dialog Systems ◽  
Problem Solving Process ◽  
Processing Architecture ◽  
Dialog Processing

As spoken natural language dialog systems technology continues to make great strides, numerous issues regarding dialog processing still need to be resolved. This book presents an exciting new dialog processing architecture that allows for a number of behaviors required for effective human-machine interactions, including: problem-solving to help the user carry out a task, coherent subdialog movement during the problem-solving process, user model usage, expectation usage for contextual interpretation and error correction, and variable initiative behavior for interacting with users of differing expertise. The book also details how different dialog problems in processing can be handled simultaneously, and provides instructions and in-depth result from pertinent experiments. Researchers and professionals in natural language systems will find this important new book an invaluable addition to their libraries.

scholarly journals What You Say or How You Say It? Depression Detection Through Joint Modeling of Linguistic and Acoustic Aspects of Speech

2021 ◽  
Author(s):  
Nujud Aloshban ◽  
Anna Esposito ◽  
Alessandro Vinciarelli
Keyword(s):  
Short Term Memory ◽  
Joint Modeling ◽  
Joint Analysis ◽  
Health Issues ◽  
Multimodal Analysis ◽  
Sequence Modeling ◽  
Depression Detection ◽  
Long Short Term Memory ◽  
Joint Representation ◽  
Better Than

AbstractDepression is one of the most common mental health issues. (It affects more than 4% of the world’s population, according to recent estimates.) This article shows that the joint analysis of linguistic and acoustic aspects of speech allows one to discriminate between depressed and nondepressed speakers with an accuracy above 80%. The approach used in the work is based on networks designed for sequence modeling (bidirectional Long-Short Term Memory networks) and multimodal analysis methodologies (late fusion, joint representation and gated multimodal units). The experiments were performed over a corpus of 59 interviews (roughly 4 hours of material) involving 29 individuals diagnosed with depression and 30 control participants. In addition to an accuracy of 80%, the results show that multimodal approaches perform better than unimodal ones owing to people’s tendency to manifest their condition through one modality only, a source of diversity across unimodal approaches. In addition, the experiments show that it is possible to measure the “confidence” of the approach and automatically identify a subset of the test data in which the performance is above a predefined threshold. It is possible to effectively detect depression by using unobtrusive and inexpensive technologies based on the automatic analysis of speech and language.

Personalized weather information for low-literate farmers using multimodal dialog systems

2021 ◽  
Author(s):  
Muhammad Qasim ◽  
Haris Bin Zia ◽  
Awais Athar ◽  
Tania Habib ◽  
Agha Ali Raza
Keyword(s):  
Dialog Systems ◽  
Weather Information
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