# gaHealth: An English-Irish Bilingual Corpus of Health Data

arXiv: https://arxiv.org/abs/2403.03575 · HTML: https://mlsanity.com/paper/2403.03575/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 25 references that are in the map.
- First posted on arXiv 6 Mar 2024.

## Builds on (most cited first, 11 of 11)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [SentencePiece: A simple and language independent subword tokenizer and detokenizer for Neural Text Processing](https://mlsanity.com/paper/1808.06226/) (2018)
- [Six Challenges for Neural Machine Translation](https://mlsanity.com/paper/1706.03872/) (2017)
- [OpenNMT: Open-source Toolkit for Neural Machine Translation](https://mlsanity.com/paper/1709.03815/) (2017)
- [Edinburgh Neural Machine Translation Systems for WMT 16](https://mlsanity.com/paper/1606.02891/) (2016)
- [Revisiting Low-Resource Neural Machine Translation: A Case Study](https://mlsanity.com/paper/1905.11901/) (2019)
- [Findings of the LoResMT 2021 Shared Task on COVID and Sign Language for Low-resource Languages](https://mlsanity.com/paper/2108.06598/) (2021)
- [Machine Translation in the Covid domain: an English-Irish case study for LoResMT 2021](https://mlsanity.com/paper/2403.01196/) (2024)
- [Transformers for Low-Resource Languages: Is F'eidir Linn!](https://mlsanity.com/paper/2403.01985/) (2024)
- [Attentive fine-tuning of Transformers for Translation of low-resourced languages @LoResMT 2021](https://mlsanity.com/paper/2108.08556/) (2021)

## Built on by (most cited first, 3 of 3)

- [Sociotechnical Effects of Machine Translation](https://mlsanity.com/paper/2503.20959/) (2025)
- [Qomhra: A Bilingual Irish and English Large Language Model](https://mlsanity.com/paper/2510.17652/) (2025)
- [SemiAdapt and SemiLoRA: Efficient Domain Adaptation for Transformer-based Low-Resource Language Translation with a Case Study on Irish](https://mlsanity.com/paper/2510.18725/) (2025)

## Abstract (arXiv)

Machine Translation is a mature technology for many high-resource language pairs. However in the context of low-resource languages, there is a paucity of parallel data datasets available for developing translation models. Furthermore, the development of datasets for low-resource languages often focuses on simply creating the largest possible dataset for generic translation. The benefits and development of smaller in-domain datasets can easily be overlooked. To assess the merits of using in-domain data, a dataset for the specific domain of health was developed for the low-resource English to Irish language pair. Our study outlines the process used in developing the corpus and empirically demonstrates the benefits of using an in-domain dataset for the health domain. In the context of translating health-related data, models developed using the gaHealth corpus demonstrated a maximum BLEU score improvement of 22.2 points (40%) when compared with top performing models from the LoResMT2021 Shared Task. Furthermore, we define linguistic guidelines for developing gaHealth, the first bilingual corpus of health data for the Irish language, which we hope will be of use to other creators of low-resource data sets. gaHealth is now freely available online and is ready to be explored for further research.