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.