Monolingual Model

Monolingual models, trained exclusively on a single language's data, offer a counterpoint to multilingual models in natural language processing. Research currently focuses on comparing their performance against multilingual counterparts across various tasks, including speech recognition, sentiment analysis, and named entity recognition, often employing transformer-based architectures like BERT and its variants. This comparative approach aims to determine the optimal model type for specific languages and tasks, considering factors like resource availability and the need to mitigate biases or security vulnerabilities. The findings inform the development of more effective and ethical NLP systems for diverse languages and applications.