Multilingual Text Simplification

Multilingual text simplification aims to automatically create simpler versions of complex texts in multiple languages, addressing accessibility challenges across diverse populations. Current research focuses on developing and evaluating multilingual models, often leveraging pre-trained multilingual language models and exploring techniques like few-shot prompting and cross-lingual transfer learning to improve performance, particularly in low-resource languages. This work is significant for enhancing access to information in various domains, such as healthcare and humanitarian aid, and for advancing the development of robust and adaptable natural language processing tools. The creation of large, multilingual datasets is crucial for driving progress in this field.