  In the current machine translation (MT) landscape, the Transformer
architecture stands out as the gold standard, especially for high-resource
language pairs. This research delves into its efficacy for low-resource
language pairs including both the English$\leftrightarrow$Irish and
English$\leftrightarrow$Marathi language pairs. Notably, the study identifies
the optimal hyperparameters and subword model type to significantly improve the
translation quality of Transformer models for low-resource language pairs.
  The scarcity of parallel datasets for low-resource languages can hinder MT
development. To address this, gaHealth was developed, the first bilingual
corpus of health data for the Irish language. Focusing on the health domain,
models developed using this in-domain dataset exhibited very significant
improvements in BLEU score when compared with models from the LoResMT2021
Shared Task. A subsequent human evaluation using the multidimensional quality
metrics error taxonomy showcased the superior performance of the Transformer
system in reducing both accuracy and fluency errors compared to an RNN-based
counterpart.
  Furthermore, this thesis introduces adaptNMT and adaptMLLM, two open-source
applications streamlined for the development, fine-tuning, and deployment of
neural machine translation models. These tools considerably simplify the setup
and evaluation process, making MT more accessible to both developers and
translators. Notably, adaptNMT, grounded in the OpenNMT ecosystem, promotes
eco-friendly natural language processing research by highlighting the
environmental footprint of model development. Fine-tuning of MLLMs by adaptMLLM
demonstrated advancements in translation performance for two low-resource
language pairs: English$\leftrightarrow$Irish and
English$\leftrightarrow$Marathi, compared to baselines from the LoResMT2021
Shared Task.
