Towards Resource-Rich Mizo and Khasi in NLP: Resource Development, Synthetic Data Generation and Model Building

In the rapidly evolving field of Natural Language Processing (NLP), Indian regional languages remain significantly underrepresented due to their limited digital presence and lack of annotated resources.This work presents the first comprehensive effort toward developing high quality linguistic datasets for two extremely low resource languages Mizo and Khasi.We introduce human annotated, gold standard datasets for three core NLP tasks: Part-of-Speech (POS) tagging, Named Entity Recognition (NER), and Keyword Identification.To overcome annotation bottlenecks in NER, we further explore a synthetic data generation pipeline involving translation from Hindi and cross-lingual word alignment.For POS tagging, we adopt and subsequently modify the Universal Dependencies (UD) framework to better suit the linguistic characteristics of Mizo and Khasi, while custom annotation guidelines are developed for NER and Keyword Identification.The constructed datasets are evaluated using multilingual language models, demonstrating that structured resource development, coupled with gradual fine-tuning, yields significant improvements in performance.This work represents a critical step toward advancing linguistic resources and computational tools for Mizo and Khasi.