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, 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