Keywords: Sinhala | Natural Language Processing | Machine Learning / Deep Learning |
Conference Papers
Imalsha Puranegedara, Themira Chathumina, Nisal Ranathunga, Nisansa de Silva, Surangika Ranathunga, and Mokanarangan Thayaparan, "Utilizing Multilingual Encoders to Improve Large Language Models for Low-Resource Languages", in Moratuwa Engineering Research Conference (MERCon), 2025. doi: 10.1109/MERCon67903.2025.11216992 
ActivePI: Nisansa de Silva
The Knowledge Representation project investigates methods for structuring, aligning, and reasoning over knowledge extracted from language, with particular emphasis on low-resource settings, domain-specific semantics, and intelligent information access.
ActivePI: Nisansa de Silva
We propose to advance natural language processing for low-resource settings by leveraging data augmentation, transfer learning, and model adaptation techniques to overcome the scarcity of annotated data and linguistic resources.
ActivePI: Nisansa de Silva
We propose to build and analyze Sinhala language resources, datasets, and models to strengthen NLP research for this low-resource language, while connecting it to English and Tamil through bilingual lexicons, embeddings, and cross-lingual methods.
