Machine Translation
2025 2nd International Conference on Artificial Intelligence and Natural Language Processing (AINLP 2025)
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 |
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 |