

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 2nd 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.


![]() | Submission Deadline March 5, 2026 | ![]() | Notification Date April 5, 2026 |
![]() | Registration Deadline April 20, 2026 | ![]() | Conference Date May 22-24, 2026 |
All accepted full papers will be published in the conference proceedings and will be submitted to EI Compendex / Scopus for indexing.
ICAHN 2025
Publication | EI Compendex丨Scopus (released soon)
Note: All submitted articles should report original research results, experimental or theoretical, not previously published or under consideration for publication elsewhere. Articles submitted to the conference should meet these criteria. We firmly believe that ethical conduct is the most essential virtue of any academics. Hence, any act of plagiarism or other misconduct is totally unacceptable and cannot be tolerated.


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

Conference Secretary: Ms. Li
Tel: 13922150104
E-Mail: icicahn@163.com
If you have any questions or inquiries, please feel free to contact us.