Machine Translation in the Covid domain: an English-Irish case study for LoResMT 2021

Neural Machine Translation (NMT) has routinely outperformed Statistical Machine Translation (SMT) when large parallel datasets are available (Crego et al., 2016; Wu et al., 2016). Furthermore, Transformer based approaches have demonstrated impressive results in moderate low-resource scenarios (Lankford et al., 2021). NMT involving Transformer model development will improve the performance in specific domains of low-resource languages (Araabi and Monz, 2020). However, the benefits of NMT are less clear when using very low-resource Machine Translation (MT) on in-domain datasets of less than 10k lines. The Irish language is a primary example of a low-resource language that will benefit from such research. This paper reports the results for the MT system developed for the English-Irish shared task at LoResMT 2021 (Ojha et al., 2021). Relevant work is presented in the background section followed by an overview of the proposed approach. The empirical findings are outlined in the results section. Finally, the key findings are presented and discussed.