About ICAHN 2026
With the rapid development of information technology, the scale and diversity of data generation has increased dramatically, especially the growth of unstructured data, which has driven changes in the fields of Natural Language Processing (NLP) and Human-Computer Interaction. 2026 International Conference on Artificial Intelligence, Human-Computer Interaction and Natural Language Processing（ICAHN 2026）will be held in Xiamen, China, from May 22-24, 2026. The aim of this conference is to bring together This conference aims to bring together researchers, scholars and engineers in the field of Natural Language Processing, Human-Computer Interaction and Artificial Intelligence to discuss the current technological frontiers, application trends and future directions. Through academic exchanges and cooperation, the conference hopes to promote cross-field research cooperation, share the latest research results, and provide a good platform for participants to present their research work. In order to ensure the academic quality of this conference and to attract more original and high-level academic papers, we are now openly soliciting contributions from teaching, researchers and students engaged in natural language processing and information processing.
Organized By
Supported by
Important Dates
 | Submission Deadline February 12, 2026 |
 | Notification Date March 20, 2026 |
 | Registration Deadline April 20, 2026 |
 | Conference Date May 22-24, 2026 |
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Conference Topics
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.
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 maintainence, 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
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
Contact
Conference Secretary: Ms. Li
Tel: 13922150104
E-Mail: aihinlp@163.com
If you have any questions or inquiries, please feel free to contact us.