Notification of Acceptancen Date



Full Paper Submission Date
☑March 29, 2026
Notification of Acceptancen Date
☑May 03, 2026
Final Paper Submission Date
☑June 03, 2026
Registration Deadline
☑June 18, 2026
Conference Dates
☑ July 03-05, 2026
Venue: Fuzhou, China
2026 3rd International Conference on Artificial Intelligence and Natural Language Processing (AINLP 2026) will be held on July 3-5, 2026, in Fuzhou, China. This conference focuses on the latest research on Artificial Intelligence and Natural Language Processing, and aims to gather experts, scholars, researchers, and practitioners in the field from all over the world to share research results, explore hot issues, and exchange new experiences and technologies. The conference aims to bring together experts, scholars, researchers and practitioners in this field from all over the world to share research results, explore hot issues, and exchange new experiences and technologies.
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
