Low Resource Maltese

Maltese, a low-resource language with a unique blend of Semitic and Romance influences, presents significant challenges for natural language processing. Current research focuses on improving Maltese language models by leveraging cross-lingual transfer from related languages, employing techniques like conditional transliteration based on word etymology to optimize model performance on downstream tasks such as part-of-speech tagging and sentiment analysis. Researchers are also investigating the interplay of phonology and etymology in Maltese morphology using computational modeling and information theory, aiming to better understand its complex morphological system. These advancements contribute to a broader effort to develop robust NLP tools for low-resource languages, ultimately improving access to technology and resources for Maltese speakers.