It is shown that even relatively small PLMs such as the 270M-parameter Gemma 3 and mBART50, when fine-tuned on a dataset of only 5k sentences, can outperform rule-based spell correctors, highlighting a practical pathway for building effective spell correction systems with limited data.
Accurately predicting daily international tourist arrivals is challenging for tourism-dependent economies like Sri Lanka, especially during its post-crisis recovery (2023–2025). Existing methods often ignore non-Google search engines, missing critical signals from key source markets like Russia, where Yandex dominates....
A black-box, inference-time diagnostic that tells these two cases apart without retraining or annotation is introduced, with the pattern holding on English-Marathi and English-Tamil, with the failure modes tracking the post-edit distribution rather than MT quality or language family.
Isuru Wijesiri, Nisansa de Silva, Kavindu Warnakulasuriya et al.· 0 citations
EnSiTa is presented, a trilingual multi-domain parallel dataset and benchmark for English, Sinhala and Tamil, and is the most extensive systematically documented multi-domain parallel data creation and benchmarking effort for low-resource MT.
Surangika Ranathunga, Nisansa de Silva, Aloka Fernando et al.· 1 citation· ⚡1
Spell correction is still a challenging problem for many languages, especially low-resource languages (LRLs). While pre-trained language models (PLMs) have been employed for spell correction, there has been no proper comparison across PLMs. We present the first empirical study on the effectiveness of the three types of...
Akesh Gunathilake, N. Karunarathna, Tharusha Bandaranayake et al.· Moratuwa Engineering Researc...· 0 citations
TripleBound is proposed, a hybrid framework for automated monolith-to-microservices decomposition that augments a heterogeneous graph neural network with weakly supervised triplet constraints derived from parser-inferred service groups based on package structure, naming conventions, and code location.
M. Weerasinghe, Himindu Kularathne, Methmini Madhushika et al.· 0 citations
This work provides a preliminary framework for low-resource Sinhala diachronic analysis, highlighting the trade-offs between model sensitivity and data availability.
This research extends an existing multilingual LLM (Llama-3-8B) to get a better coverage for Sinhala and enhances the LLM tokenizer with Sinhala specific vocabulary and performs continual pre-training on a 10 million sentence Sinhala corpus, resulting in the SinLlama model.
The Colombo Tea Auction (CTA) plays a vital role in determining global tea prices, yet the relationship between local weather conditions and price behavior across different tea catalogues has not been thoroughly explored. In this study, we develop a novel, structured dataset by extracting information from 105 weekly br...
H. Mallawarachchi, Senilka Madurapperumage, Nadil Kulathunge et al.· 0 citations
A survey and comparative analysis of NLP-based Automatic Deception Detection focusing on the legal domain and the evolution from feature-based machine learning to Large Language Model (LLM) approaches are presented, showing strong domain sensitivity.
T. Samaradiwakara, Nisansa de Silva, George C. Lobb· arXiv.org· 0 citations
We present HelaBERT, a family of two BERT-based masked language models pre-trained from scratch on approximately 1 billion tokens of Sinhala text sourced from MADLAD-400, CulturaX, and a custom corpus comprising news articles, Sinhala Wikipedia, and web crawl data. HelaBERT-Small (~23.3M parameters, 6 layers) and HelaB...
Thisen Ekanayake, Nisansa de Silva· 0 citations
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