Language Data

Language data research focuses on developing and improving methods for collecting, processing, and utilizing textual and spoken language data to train and enhance natural language processing (NLP) models. Current research emphasizes addressing data scarcity in low-resource languages, mitigating biases and ethical concerns in data collection, and improving model performance through techniques like multilingual fine-tuning, self-supervised learning, and data augmentation strategies. This work is crucial for advancing NLP capabilities across diverse languages and cultures, impacting applications ranging from machine translation and speech recognition to sentiment analysis and hate speech detection.