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.

机器翻译是许多高资源语言对的成熟技术，然而在低资源语言的情况下，缺乏用于开发翻译模型的平行数据集。本研究开发了针对低资源英语到爱尔兰语领域的特定健康领域的数据集，实验证明使用领域内数据集的好处。通过比较于 LoResMT2021 共享任务的最佳模型，使用 gaHealth 数据集开发的模型在翻译健康相关数据方面的 BLEU 分数最高可以提高 22.2 个百分点（40%）。此外，我们定义了开发 gaHealth 的语言指南，这是为爱尔兰语健康数据开发的第一个双语语料库，我们希望能对其他低资源数据集的创建者有所帮助。gaHealth 现在已经免费在线提供，并可用于进一步研究。

gaHealth：一个英爱双语健康数据语料库

gaHealth: An English-Irish Bilingual Corpus of Health Data

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.

该研究旨在研究机器翻译中 Transformer 模型在低资源语言对（英爱尔兰语和英马拉地语）中的效果，并通过优化超参数和子词模型类型来显著提高低资源语言对的翻译质量。此外，还引入了 adaptNMT 和 adaptMLLM 两个开源应用程序，简化了神经机器翻译模型的开发、微调和部署过程，使其更容易被开发人员和翻译人员使用。