PI: Surangika Ranathunga
The Data Quality Estimation and Improvement project focuses on analysing, measuring, and enhancing the quality of datasets used in natural language processing, particularly for multilingual and low-resource language applications.
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.
Dissertations
W.A.S.A Fernando, "Data Augmentation to Induce High Quality Parallel Data for Low-Resource Neural Machine Translation", University of Moratuwa, 2025
Theses: MSc Minor Component Research
Sadeep Gunathilaka, "Automated User Review Analysis To Facilitate Potential Mobile Application Evolution", University of Moratuwa, 2025
Conference Papers
Aloka Fernando, Nisansa de Silva, Menan Velayuthan, Charitha Rathnayaka, and Surangika Ranathunga, "Improving the quality of Web-mined Parallel Corpora of Low-Resource Languages using Debiasing Heuristics", in Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, 2025, pp. 28252--28269. doi: 10.18653/v1/2025.emnlp-main.1435
