NLP in Multilingual Contexts: Challenges and Solutions
NLP in Multilingual Contexts: Challenges and Solutions NLP has made strong progress, but multilingual contexts still bring specific hurdles. Data gaps, diverse scripts, and cultural nuances can slow the development of reliable systems. For global products, models must handle languages with different alphabets, writing styles, and even code-switching. Common challenges include data scarcity for many languages, especially those with fewer speakers. Varied scripts and rich morphology complicate tokenization and model input. Dialects and code-switching inside sentences add extra noise. Bias and uneven representation across languages can hurt fairness. Evaluation often relies on high-resource benchmarks, which makes cross-language comparison hard. Privacy and legal limits also shape what data can be used for training. ...