The topics of interest for submission include, but are not limited to:
◕ Machine Learning for NLP
Graph-based methods
Knowledge-augmented methods
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
◕ 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
◕ language generation
Human evaluation
Automatic evaluation
Multilingualism
Efficient models
Few-shot generation
Analysis
Domain adaptation
Data-to-text generation
Text-to-text generation
Inference methods
Model architectures
Retrieval-augmented generation
Interactive and collaborative generation
◕ Machine Translation
Automatic evaluation
Biases
Domain adaptation
Efficient inference for MT
Efficient MT training
Few-/Zero-shot MT
Human evaluation
Interactive MT
MT deployment and maintenance
MT theory
Modeling
Multilingual MT
Multimodality
Online adaptation for MT
Parallel decoding/non-autoregressive MT
Pre-training for MT
Scaling
Speech translation
Code-switching translation
Vocabulary learning
◕ Interpretability and Analysis of Models in NLP
Adversarial attacks/examples/training
Calibration/uncertainty
Counterfactual/contrastive explanations
Data influence
Data shortcuts/artifacts
Explanation faithfulness
Feature attribution
Free-text/natural language explanation
Hardness of samples
Hierarchical & concept explanations
Human-subject application-grounded evaluations
Knowledge tracing/discovering/inducing
Probing
Robustness
Topic modeling