  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.
