NagaNLP: Advancing NLP for Low-Resource Languages with Synthetic Data

This research explores a practical approach to Natural Language Processing in a low-resource setting, addressing a common challenge in the field. The use of human-in-the-loop synthetic data generation offers a potentially scalable solution for languages lacking extensive training datasets.

• •Addresses the challenge of NLP for low-resource languages.
• •Employs a human-in-the-loop approach for synthetic data generation.
• •Focuses on Nagamese Creole as a case study.