Multilingual NLP: Challenges and Innovations

This lecture discusses the significance of multilingual natural language processing (NLP) in addressing the digital divide among languages. The instructor highlights the current state of multilingual language models and the challenges faced in scaling these models to accommodate numerous languages. With approximately 7,000 languages spoken globally, the lecture emphasizes the disparity in representation, as only a few hundred languages are adequately represented online. The instructor explains the two main approaches to multilingual NLP: monolingual NLP in multiple languages and cross-lingual NLP, which aims to learn languages jointly. The discussion includes the importance of data quality and availability, as well as the cultural biases inherent in existing NLP models. The lecture also covers the concept of cross-lingual transfer, where knowledge from high-resource languages is applied to low-resource languages. Finally, the instructor addresses the curse of multilinguality, where adding more languages can lead to decreased performance, and suggests potential solutions to improve multilingual NLP systems.