# NSINA: A News Corpus for Sinhala

arXiv: https://arxiv.org/abs/2403.16571 · HTML: https://mlsanity.com/paper/2403.16571/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 22 references that are in the map.
- First posted on arXiv 25 Mar 2024.

## Builds on (most cited first, 13 of 13)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding](https://mlsanity.com/paper/1804.07461/) (2018)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [mT5: A massively multilingual pre-trained text-to-text transformer](https://mlsanity.com/paper/2010.11934/) (2020)
- [BLEURT: Learning Robust Metrics for Text Generation](https://mlsanity.com/paper/2004.04696/) (2020)
- [Towards a Cleaner Document-Oriented Multilingual Crawled Corpus](https://mlsanity.com/paper/2201.06642/) (2022)
- [Extending Multilingual BERT to Low-Resource Languages](https://mlsanity.com/paper/2004.13640/) (2020)
- [Survey on Publicly Available Sinhala Natural Language Processing Tools and Research](https://mlsanity.com/paper/1906.02358/) (2019)
- [Machine Generation and Detection of Arabic Manipulated and Fake News](https://mlsanity.com/paper/2011.03092/) (2020)
- [BERTifying Sinhala -- A Comprehensive Analysis of Pre-trained Language Models for Sinhala Text Classification](https://mlsanity.com/paper/2208.07864/) (2022)

## Built on by (most cited first, 6 of 6)

- [Text Generation Models for Luxembourgish with Limited Data: A Balanced Multilingual Strategy](https://mlsanity.com/paper/2412.09415/) (2024)
- [SinhalaMMLU: A Comprehensive Benchmark for Evaluating Multitask Language Understanding in Sinhala](https://mlsanity.com/paper/2509.03162/) (2025)
- [Overview of the First Workshop on Language Models for Low-Resource Languages (LoResLM 2025)](https://mlsanity.com/paper/2412.16365/) (2024)
- [Linguistic Analysis of Sinhala YouTube Comments on Sinhala Music Videos: A Dataset Study](https://mlsanity.com/paper/2501.18633/) (2025)
- [Swa-bhasha Resource Hub: Romanized Sinhala to Sinhala Transliteration Systems and Data Resources](https://mlsanity.com/paper/2507.09245/) (2025)
- [MUNIChus: Multilingual News Image Captioning Benchmark](https://mlsanity.com/paper/2603.10613/) (2026)

## Abstract (arXiv)

The introduction of large language models (LLMs) has advanced natural language processing (NLP), but their effectiveness is largely dependent on pre-training resources. This is especially evident in low-resource languages, such as Sinhala, which face two primary challenges: the lack of substantial training data and limited benchmarking datasets. In response, this study introduces NSINA, a comprehensive news corpus of over 500,000 articles from popular Sinhala news websites, along with three NLP tasks: news media identification, news category prediction, and news headline generation. The release of NSINA aims to provide a solution to challenges in adapting LLMs to Sinhala, offering valuable resources and benchmarks for improving NLP in the Sinhala language. NSINA is the largest news corpus for Sinhala, available up to date.