  In this study, a human evaluation is carried out on how hyperparameter
settings impact the quality of Transformer-based Neural Machine Translation
(NMT) for the low-resourced English--Irish pair. SentencePiece models using
both Byte Pair Encoding (BPE) and unigram approaches were appraised. Variations
in model architectures included modifying the number of layers, evaluating the
optimal number of heads for attention and testing various regularisation
techniques. The greatest performance improvement was recorded for a
Transformer-optimized model with a 16k BPE subword model. Compared with a
baseline Recurrent Neural Network (RNN) model, a Transformer-optimized model
demonstrated a BLEU score improvement of 7.8 points. When benchmarked against
Google Translate, our translation engines demonstrated significant
improvements. Furthermore, a quantitative fine-grained manual evaluation was
conducted which compared the performance of machine translation systems. Using
the Multidimensional Quality Metrics (MQM) error taxonomy, a human evaluation
of the error types generated by an RNN-based system and a Transformer-based
system was explored. Our findings show the best-performing Transformer system
significantly reduces both accuracy and fluency errors when compared with an
RNN-based model.
