Multilingual Classification

Multilingual classification aims to build machine learning models capable of categorizing text data across multiple languages, overcoming the limitations of monolingual approaches. Current research heavily utilizes transformer-based architectures, often fine-tuned on multilingual datasets, and explores techniques like data augmentation and novel loss functions to address challenges such as class imbalance and noise in real-world data. This field is crucial for applications like social media content moderation, fake news detection, and automotive fault diagnosis, driving advancements in cross-lingual understanding and robust model development.