Investigating Neural Machine Translation for Low-Resource Languages:
  Using Bavarian as a Case Study
Wan-Hua Her Udo Kruschwitz
Machine Translation has made impressive progress in recent years offering
close to human-level performance on many languages, but studies have primarily
focused on high-resource languages with broad online presence and resources.
With the help of growing Large Language Models, more and more low-resource
languages achieve better results through the presence of other languages.
However, studies have shown that not all low-resource languages can benefit
from multilingual systems, especially those with insufficient training and
evaluation data. In this paper, we revisit state-of-the-art Neural Machine
Translation techniques to develop automatic translation systems between German
and Bavarian. We investigate conditions of low-resource languages such as data
scarcity and parameter sensitivity and focus on refined solutions that combat
low-resource difficulties and creative solutions such as harnessing language
similarity. Our experiment entails applying Back-translation and Transfer
Learning to automatically generate more training data and achieve higher
translation performance. We demonstrate noisiness in the data and present our
approach to carry out text preprocessing extensively. Evaluation was conducted
using combined metrics: BLEU, chrF and TER. Statistical significance results
with Bonferroni correction show surprisingly high baseline systems, and that
Back-translation leads to significant improvement. Furthermore, we present a
qualitative analysis of translation errors and system limitations.