Foundations of Natural Language Processing


• Syntax, Parsing and Structured Prediction
• Morphology, Phonology and Lexical Semantics
• Semantics, Discourse and Pragmatics
• Linguistic Resources, Corpora and Benchmark Creation
• Knowledge Representation, Knowledge Graphs and Neuro Symbolic NLP

Machine Learning for NLP


• Deep Learning Architectures for NLP
• Large Language Models (LLMs): Training, Alignment and Evaluation
• Efficient NLP Models: Compression, Distillation, Quantization, Pruning
• Transfer Learning, Domain Adaptation and Low Resource NLP
• Continual, Lifelong and Online Learning for NLP
• Reinforcement Learning for Language Tasks
• Hybrid Statistical, Neural and Knowledge Based Methods

Data Centric and Synthetic Data NLP


• Synthetic Data Generation for NLP
• Data Quality, Filtering and Dataset Governance
• Automatic Dataset Creation for Low Resource Languages
• Contamination DetMultimodal and Speech Processing ection and Dataset Auditing

Generative and Interactive NLP


• Text Generation, Summarization and Controlled Generation
• Paraphrasing, Entailment and Natural Language Inference
• Dialogue Systems, Conversational AI and Chatbots
• Question Answering, Reasoning and Explainable NLP
• Narrative Understanding and Story Generation
• Retrieval Augmented Generation (RAG) and Knowledge Grounded Models

Reasoning, Planning and Advanced Capabilities


• Mathematical and Symbolic Reasoning
• Logical Inference and Formal Verification
• Multi Step Reasoning and Chain of Thought Models
• Planning, Tool Use and Function Calling Models

Long Context, Memory and Architecture Innovations


• Long Context Transformers and Efficient Attention
• Memory Augmented Language Models
• Document Level and Multi Document Reasoning
• Scalable and Distributed NLP Architectures

Multimodal and Speech Processing


• Vision Language Models (VLMs)
• Audio Language and Speech Language Models
• Speech Recognition, Synthesis and Spoken Language Understanding
Video Language Understanding and Generation Multimodal Dialogue and Interactive Systems

Agentic and Autonomous Language Systems


• Agentic AI and Autonomous Language Agents
• Multi Agent Communication and Collaboration
• Tool Using Models and API Driven Language Systems
• Embodied AI and Robotics Language Interfaces

Responsible, Safe and Trustworthy NLP


• Bias, Fairness and Ethical NLP
• Safety, Hallucination Detection and Robustness
• Privacy Preserving NLP (Federated Learning, Differential Privacy)
• Security, Adversarial Attacks and Defenses
• Environmental Impact and Sustainable NLP
• AI Governance, Policy and Societal Impact

Evaluation, Benchmarking and Reproducibility


• Evaluation Metrics and Benchmark Design
• Meta Evaluation and Evaluation Reliability
• Human AI Comparative Evaluation
• Reproducibility, Transparency and Open Science

Applied and Domain Specific NLP


• Machine Translation and Cross Lingual NLP
• Information Extraction, Text Mining and Neural Search
• Biomedical, Clinical and Healthcare NLP
• Legal, Financial and Policy Driven NLP
• Scientific NLP and Research Automation
• NLP for Software Engineering, Code Understanding and Program Synthesis
• NLP for Education, Assessment and Learning Technologies
• NLP for Social Media, Misinformation and Digital Safety
• NLP for Climate Science, Sustainability and Environmental Modeling

Cognitive, Human Centered and Interdisciplinary NLP


• Cognitive Modeling and Psycholinguistics
• Brain Language Interfaces (EEG/fMRI + NLP)
• Human AI Collaboration and Co Creation
• Human in the Loop Learning and Annotation
• NLP for HCI and Interactive Systems

Hard copy of the proceedings will be distributed during the Conference. The softcopy will be available on AIRCC digital library.