  This paper investigates the impact of data volume and the use of similar
languages on transfer learning in a machine translation task. We find out that
having more data generally leads to better performance, as it allows the model
to learn more patterns and generalizations from the data. However, related
languages can also be particularly effective when there is limited data
available for a specific language pair, as the model can leverage the
similarities between the languages to improve performance. To demonstrate, we
fine-tune mBART model for a Polish-English translation task using the OPUS-100
dataset. We evaluate the performance of the model under various transfer
learning configurations, including different transfer source languages and
different shot levels for Polish, and report the results. Our experiments show
that a combination of related languages and larger amounts of data outperforms
the model trained on related languages or larger amounts of data alone.
Additionally, we show the importance of related languages in zero-shot and
few-shot configurations.
