2026 9th International Conference on Machine Learning and Natural Language Processing (MLNLP 2026)

*【Call for papers】:

Track 1: Machine Learning (ML) ​

Foundations: Supervised, unsupervised, and semi-supervised learning.
Deep Learning: Neural network architectures, attention mechanisms, and graph neural networks.
Learning Paradigms: Reinforcement learning, transfer learning, and multi-task learning.
Trustworthy AI: Explainable AI (XAI), robustness, fairness, and privacy-preserving machine learning.
Optimization: Large-scale optimization algorithms and evolutionary computing.
Edge & Distributed ML: Federated learning and on-device machine learning. ​

Track 2: Natural Language Processing (NLP) ​

Large Language Models (LLMs): Pre-training, fine-tuning, prompting, and alignment (RLHF).
Linguistic Analysis: Syntactic parsing, semantic analysis, and pragmatic reasoning.
Core NLP Tasks: Named entity recognition, sentiment analysis, and summarization.
Dialogue & Interactive Systems: Chatbots, multi-turn dialogue management, and human-computer interaction.
Machine Translation: Neural machine translation and multilingual processing.
Information Retrieval: Question answering, document ranking, and knowledge graph construction. ​

Track 3: Track 3: ML & NLP Convergence (Emerging Trends) ​

Multimodal Learning: Integration of vision, audio, and text (Vision-Language Models).
Cognitive Computing: Language understanding inspired by human cognition.
AI for Science: Applications of MLNLP in healthcare, finance, and social sciences.
Efficiency & Sustainability: Green AI, model compression, and efficient inference.
Creative AI: Controllable text generation and AI-aided content creation.
Ethics & Society: Detection of AI-generated content (Deepfake text) and bias mitigation.

(for more topics: https://www.mlnlp2026.net/topic.html)