Cross Lingual Learning

Cross-lingual learning (CLL) aims to leverage knowledge from high-resource languages to improve performance in low-resource languages, addressing the data scarcity problem in natural language processing. Current research focuses on adapting existing multilingual models like mBERT and XLM-R through techniques such as meta-learning, in-context learning with large language models (LLMs), and innovative encoding methods like morpheme-based byte encoding (MYTE) to mitigate biases and improve performance across diverse languages. These advancements are significant for expanding the reach of NLP applications to a wider range of languages and facilitating research in areas like mental health prediction and misinformation detection where data is often limited.