◕ 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

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