Cross Lingual Text Classification

Cross-lingual text classification aims to automatically categorize text across multiple languages, overcoming the limitations of data scarcity in low-resource languages. Current research focuses on improving zero-shot and few-shot cross-lingual transfer using techniques like in-context learning, multilingual language models (MLLMs), and knowledge distillation from high-performing monolingual models, often employing architectures such as SBERT and mT5. These advancements are crucial for bridging the language gap in NLP applications, enabling broader access to information processing and analysis across diverse linguistic contexts.