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.
PI: Nisansa de Silva
We propose to advance automatic summarization and generation techniques, with a focus on aspect-aware methods that capture fine-grained information and sentiment within concise textual representations.
Theses: MSc Minor Component Research
Kushan Hewapathirana, "Towards Multi-document Summarisation in Low-resource Settings", University of Moratuwa, 2025
Conference Papers
Kushan Hewapathirana, Nisansa de Silva, and C D Athuraliya, "M2DS: Multilingual Dataset for Multi-document Summarisation", in International Conference on Computational Collective Intelligence, 2024, pp. 219--231. doi: 10.1007/978-3-031-70248-8_17
Kushan Hewapathirana, Nisansa de Silva, and C D Athuraliya, "Multi-Document Summarization: A Comparative Evaluation", in 2023 IEEE 17th International Conference on Industrial and Information Systems (ICIIS), 2023, pp. 19--24. doi: 10.1109/ICIIS58898.2023.10253581
Extended Abstracts
Kushan Hewapathirana, Nisansa de Silva, and C. D. Athuraliya, "Adapter-based Fine-tuning for PRIMERA", in 1st Applied Data Science \& Artificial Intelligence Symposium, April. 2025.
