Intent Recognition for Human-Machine Interactions (SpringerBriefs in Computer Science)

✍ Scribed by Hua Xu, Hanlei Zhang, Ting-En Lin

• Publisher: Springer
• Year: 2023
• Tongue: English
• Leaves: 162
• Category: Library

✦ Synopsis

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.

✦ Table of Contents

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
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
5.4 Conclusion
References
Chapter 6: Unknown Intent Detection Based on Large-Margin Cosine Loss
6.1 Introduction
6.2 New Intent Detection Model Based on Large Margin Cosine Loss Function
6.2.1 Large Margin Cosine Loss (LMCL)
6.3 Experiments
6.3.1 Datasets
6.3.2 Baselines
6.3.3 Experiment Setting
Hyper-Parameter Setting
6.3.4 Experiment Results
6.4 Conclusion
References
Chapter 7: Unknown Intention Detection Method Based on Dynamic Constraint Boundary
7.1 Introduction
7.2 The Frame Structure of the Model
7.3 The Main Approach
7.3.1 Intent Representation
7.3.2 Pre-Training
7.3.3 Adaptive Decision Boundary Learning
Decision Boundary Formulation
Boundary Learning
7.3.4 Open Classification with Decision Boundary
7.4 Experiments
7.4.1 Datasets
7.4.2 Baselines
7.4.3 Experiment Settings
Evaluation Metrics
7.4.4 Experiment Results
7.5 Discussion
7.5.1 Boundary Learning Process
7.5.2 Effect of Decision Boundary
7.5.3 Effect of Labeled Data
7.5.4 Effect of Known Classes
7.6 Conclusion
References
Part IV: Discovery of Unknown Intents
Chapter 8: Discovering New Intents Via Constrained Deep Adaptive Clustering with Cluster Refinement
8.1 Introduction
8.2 New Intent Discovery Model Based on Self-Supervised Constrained Clustering
8.2.1 Intent Representation
8.2.2 Pairwise-Classification with Similarity Loss
Supervised Step
Unsupervised Step
8.2.3 Cluster Refinement with KLD Loss
8.3 Experiments
8.3.1 Datasets
8.3.2 Baseline
8.3.3 Experiment Settings
Evaluation Metrics
Hyper Parameter
8.3.4 Experiment Results
Ablation Study
Effect of the Number of Clusters
Effect of Labeled Data
Effect of Unknown Data
Performance on Imbalanced Dataset
Error Analysis
8.4 Conclusion
References
Chapter 9: Discovering New Intents with Deep Aligned Clustering
9.1 Introduction
9.2 Deep Aligned Clustering
9.2.1 Intent Representation
9.2.2 Transferring Knowledge From Known Intents
Pre-training
Predict K
9.2.3 Deep Aligned Clustering
Unsupervised Learning by Clustering
Self-Supervised Learning with Aligned Pseudo-Labels
9.3 Experiments
9.3.1 Datasets
9.3.2 Baselines
Unsupervised
Semi-supervised
9.3.3 Experiment Settings
Evaluation Metrics
Hyper Parameters
Experiment Results
Effect of the Alignment Strategy
Estimate K
Effect of Known Class Ratio
Effect of the Number of Classes
9.4 Conclusion
References
Part V: Dialogue Intent Recognition Platform
Chapter 10: Experiment Platform for Dialogue Intent Recognition Based on Deep Learning
10.1 Introduction
10.2 Open Intent Recognition Platform
10.2.1 Data Management
10.2.2 Models
Open Intent Detection
Open Intent Discovery
10.2.3 Training and Evaluation
10.2.4 Result Analysis
Open Intent Detection
Open Intent Discovery
10.3 Pipeline Framework
10.4 Experiments
10.5 Conclusion
References
Part VI: Summary and Future Work
Chapter 11: Summary
Appendix