PI: Surangika Ranathunga
We propose to create a multi-domain Neural Machine Translation (NMT) System for Sinhala, Tamil, and English, the official languages of Sri Lanka.
PI: 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.
PI: 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.
Theses: MSc Major Component Research
Menan Velayuthan, "Multi-Domain Neural Machine Translation with Knowledge Distillation For Low Resource Languages", University of Moratuwa, 2025
Workshop Papers
Menan Velayuthan, Nisansa De Silva, and Surangika Ranathunga, "Encoder-Aware Sequence-Level Knowledge Distillation for Low-Resource Neural Machine Translation", in Proceedings of the Eighth Workshop on Technologies for Machine Translation of Low-Resource Languages (LoResMT 2025), 2025, pp. 161--170.
