Language Agnostic

Language-agnostic approaches in natural language processing aim to develop models and techniques that perform effectively across multiple languages without requiring language-specific training data or adaptations. Current research focuses on creating language-neutral representations using multilingual pretrained language models (mPLMs), convolutional neural networks (CNNs), and other machine learning methods, often incorporating techniques like contrastive learning and knowledge distillation to improve performance and robustness. This work is significant because it addresses the resource scarcity problem in NLP, enabling broader accessibility of technologies like machine translation, named entity recognition, and question answering to low-resource languages, and facilitating cross-lingual knowledge sharing.