The topics of interest include, but are not limited to:Machine Learning for NLP
Graph-based methods
Knowledge-augmented methods
Knowledge engineering
Artificial intelligence
Logic programming
Human-computer interaction
Deep learning
Signal processing
Information extraction
Natural language inference
Multi-task learning)
Self-supervised learning
Contrastive learning
Generation model
Data augmentation
Word embedding
Structured prediction
Transfer learning / domain adaptation
Representation learning
泛化 Generalization
Model compression methods
Parameter-efficient finetuning
Few-shot learning
Reinforcement learning
Optimization methods
Continual learning
Adversarial training
Meta learning
Causality
Graphical models
Human-in-a-loop / Active learning
Interpretability and Analysis of Models in NLP & NLP Applications
Calibration/uncertainty
Counterfactual/contrastive explanations
Data influence
Data shortcuts/artifacts
Explantion faithfulness
Feature attribution
Free-text/natural language explanation
Hardness of samples
Hierarchical & concept explanations
Human-subject application-grounded evaluations
Knowledge tracing/discovering/inducing
◕ NLP Applications
Educational applications, GEC, essay scoring
Hate speech detection
Multimodal applications
Code generation and understanding
Fact checking, rumour/misinformation detection
Healthcare applications, clinical NLP
Financial/business NLP
Legal NLP
Mathematical NLP
Security/privacy
Historical NLP
Knowledge graph
Please send the full paper(word+pdf) to Submission System : 
