Quality and Appropriateness of Large Text Datasets for Irish NLP
Proceedings of the SIGUL 2026 Joint Workshop with ELE, EURALI, and DCLRL "Towards Inclusivity and Equality: Language Resources and Technologies for Under-Resourced and Endangered Languages
Abstract
The value of high-quality datasets for training essential language tools has long been recognised for NLP research. Despite the importance of such datasets, most language data available for training consists of large, automatically curated corpora, often scraped from web content. The quality of such datasets is often an unknown factor. This presents a problem for already low-resourced languages (such as Irish), as existing datasets may not provide adequate, representative language data for training effective models. This paper examines existing monolingual and parallel Irish text corpora to evaluate the quality of the language data, through manual review, automatic metrics, and LLMs as judges.