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2026 3rd International Conference on Artificial Intelligence and Natural Language Processing
(AINLP 2026)


☞ Topics

☀ Important Dates

Full Paper Submission Date

March 292026

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

about

About AINLP 2026

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.

cfp

Call For Papers

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


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