Intent recognition for human-machine interactions / Hua Xu, Hanlei Zhang, Ting-En Lin.
Xu, Hua.; Zhang, Hanlei.; Lin, Ting-En.
2023
QA76.9.H85
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Title Intent recognition for human-machine interactions / Hua Xu, Hanlei Zhang, Ting-En Lin.
Author Xu, Hua.
ISBN 9789819938858 (electronic bk.)
9819938856 (electronic bk.)
9819938848
9789819938841
DOI https://doi.org/10.1007/978-981-99-3885-8
Publication Details Singapore : Springer, 2023.
Language English
Description 1 online resource (162 p.).
Item Number 10.1007/978-981-99-3885-8 doi
Call Number QA76.9.H85
Dewey Decimal Classification 004.019
Summary Natural interaction is one of the hottest research issues in human-computer interaction. At present, there is an urgent need for intelligent devices (service robots, virtual humans, etc.) to be able to understand intentions in an interactive dialogue. Focusing on human-computer understanding based on deep learning methods, the book systematically introduces readers to intention recognition, unknown intention detection, and new intention discovery in human-computer dialogue. This book is the first to present interactive dialogue intention analysis in the context of natural interaction. In addition to helping readers master the key technologies and concepts of human-machine dialogue intention analysis and catch up on the latest advances, it includes valuable references for further research.
Note 5.4 Conclusion
Bibliography, etc. Note References -- Part III: Unknown Intent Detection -- Chapter 5: Unknown Intent Detection Method Based on Model Post-Processing -- 5.1 Introduction -- 5.2 A Post-Processing for New Intent Detection -- 5.2.1 Classifiers -- BiLSTM -- CNN + CNN -- 5.2.2 SofterMax -- Temperature Scaling -- Probability Calibration -- Decision Boundary -- 5.2.3 Deep Novelty Detection -- 5.2.4 SMDN -- 5.3 Experiments -- 5.3.1 Datasets -- 5.3.2 Baselines -- 5.3.3 Experiment Settings -- Evaluation -- Hyper-Parameters -- 5.3.4 Experiment Results -- Single-Turn Dialogue Datasets -- Multi-Turn Dialogue Dataset
Includes bibliographical references.
Access Note Access limited to authorized users.
Source of Description Description based on print version record.
Online resource; title from PDF title page (SpringerLink, viewed September 12, 2023).
Added Author Zhang, Hanlei.
Lin, Ting-En.
Series SpringerBriefs in computer science.
Available in Other Form Intent Recognition for Human-Machine Interactions
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Record Appears in Online Resources > Ebooks
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Intro
Preface
Contents
List of Figures
List of Tables
About the Authors
Part I: Overview
Chapter 1: Dialogue System
1.1 Review of Dialogue System
References
Chapter 2: Intent Recognition
2.1 Review of the Literature on Intent Representation
2.1.1 Discrete Representation
One Hot Representation
Bag of Word
Term Frequency-Inverse Document Frequency (TF-IDF)
N-gram
2.1.2 Distributed Representation
Matrix-Based Distributed Representation
Neural Network-Based Distributed Representation
2.1.3 Summary

2.2 Review of Known Intent Classification
2.2.1 Review of Single-Model Intent Classification
2.2.2 Review of Bi-model Intent Classification
2.2.3 Summary
2.3 Review of Unknown Intent Detection
2.3.1 Unknown Intent Detection Based on Traditional Discriminant Model
2.3.2 Unknown Intent Detection Based on Open Set Recognition in Computer Vision
2.3.3 Unknown Intent Detection Based on Out-of-Domain Detection
2.3.4 Unknown Intent Detection Based on Other Methods
2.3.5 Summary
2.4 Review of New Intent Discovery
2.4.1 New Intent Discovery Based on Unsupervised Clustering

2.4.2 New Intent Discovery Based on Semi-Supervised Clustering
2.4.3 Summary
2.5 Conclusion
References
Part II: Intent Classification
Chapter 3: Intent Classification Based on Single Model
3.1 Introduction
3.2 Comparison Systems
3.2.1 Baseline Systems
3.2.2 NNLM-Based Utterance Classifier
3.2.3 RNN-Based Utterance Classifier
3.2.4 LSTM- and GRU-Based Utterance Classifier
3.3 Experiments
3.3.1 Datasets
3.3.2 Experiment Settings
3.3.3 Experiment Results
3.4 Conclusion
References

Chapter 4: A Dual RNN Semantic Analysis Framework for Intent Classification and Slot
4.1 Introduction
4.2 Intent Classification and Slot Filling Task Methods
4.2.1 Deep Neural Network for Intent Detection
4.2.2 Recurrent Neural Network for Slot Filling
4.2.3 Joint Model for Two Tasks
4.3 Bi-Model RNN Structures for Joint Semantic Frame Parsing
4.3.1 Bi-model Structure with a Decoder
4.3.2 Bi-Model Structure without a Decoder
4.3.3 Asynchronous Training
4.4 Experiments
4.4.1 Datasets
4.4.2 Experiment Settings
4.4.3 Experiment Results
4.5 Conclusion

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