We invite submissions of original research articles, case studies, and review papers on the topics related to Artificial Intelligence and Natural Language Processing for the International Conference on Artificial Intelligence and Natural Language Processing. The conference aims to bring together researchers, engineers, and practitioners from around the world to exchange ideas and present the latest research advancements in the field.

Track1: 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

Track 2: 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

Track 3: 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

Track 4: 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

Track 5: 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

All submitted papers will be reviewed by at least two independent reviewers for quality, originality, relevance, and clarity.