2025 International Conference on Natural Language Processing and Signal Processing(NLPSP 2025) will bring together leading researchers, engineers and scientists in the domain of interest from around the world.
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 |
◕ 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 |
◕ Interpretability and Analysis of Models in NLP Adversarial attacks/examples/training Calibration/uncertainty Counterfactual/contrastive explanations Data influence Data shortcuts/artifacts Explantion 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 |