BSc Eng. (Hons) (Moratuwa)
Keywords: Sinhala | Machine Learning / Deep Learning | Natural Language Processing | Parallel Corpora | Web-mined Parallel Corpora | Low-resource Languages | Parallel Data Curation |
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
Surangika Ranathunga, Nisansa de Silva, Menan Velayuthan, Aloka Fernando, and Charitha Rathnayake, "Quality Does Matter: A Detailed Look at the Quality and Utility of Web-Mined Parallel Corpora", in Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers), St. Julian{'}s, Malta: Association for Computational Linguistics, 03. 2024, pp. 860--880. 
Preprints
Surangika Ranathunga, Nisansa de Silva, Aloka Fernando, Kavindu Warnakulasuriya, Isuru Wijesiri, Menan Velayuthan, Charitha Rathnayaka, Thivaharan Varatharajan, Sajeevi Silva, Piumi Kandanaarachchi, and Uthayasanker Thayasivam, "EnSiTa-A Trilingual Multi-Domain Parallel Dataset and Benchmark for Domain-Specific Machine Translation", ResearchGate, 2026
ActivePI: 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.
ActivePI: 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.
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.
