Multiple major hurdles lie ahead in the development of multilingual NLP models. A primary problem that impedes the development of efficient models is the deficiency of ...
Multiple major hurdles lie ahead in the development of multilingual NLP models. A primary problem that impedes the development of efficient models is the deficiency of high-quality annotated data for many languages, particularly those with lower resources. Further complicating the task of developing well-generalizable models across languages is linguistic variety, which includes variations in syntax, semantics, and cultural background. The work becomes even more difficult when managing code-switching and differing skill levels in bilingual environments. In addition, there may be differences in model performance; models with higher resource requirements typically perform better, which might result in unequal access to NLP technology. Novel strategies for data collecting, model training, and assessment techniques are needed to meet these obstacles.