Morphologically Rich Language

Morphologically rich languages (MRLs), characterized by complex word structures and high ambiguity, pose significant challenges for natural language processing (NLP). Current research focuses on improving language model performance on MRLs by exploring various tokenization strategies (character-level vs. subword), incorporating explicit morphological knowledge into pre-training, and developing joint neural architectures that simultaneously address morphological segmentation and syntactic parsing. These advancements aim to bridge the performance gap between MRLs and other languages in various NLP tasks, ultimately improving applications like machine translation, part-of-speech tagging, and question answering for speakers of these languages.