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.

本研究探讨了数据量和使用类似语言对于机器翻译任务中的迁移学习的影响，研究发现，在迁移学习中，拥有更多的数据通常会导致更好的性能，然而，相关语言在特定语言对的数据有限时也可以特别有效，最终通过使用 OPUS-100 数据集对 mBART 模型进行微调来证明结果。实验表明，相关语言和更多的数据的组合比单独使用两者能够更好的提高模型性能，同时还表明了相关语言在零样本和小样本时的重要性。

利用迁移学习改进波兰语到英语神经机器翻译：数据量和语言相似性的影响

Improving Polish to English Neural Machine Translation with Transfer  Learning: Effects of Data Volume and Language Similarity

We report on our system for the shared task on discriminating between similar
languages (DSL 2016). The system uses only byte representations in a deep
residual network (ResNet). The system, named ResIdent, is trained only on the
data released with the task (closed training). We obtain 84.88% accuracy on
subtask A, 68.80% accuracy on subtask B1, and 69.80% accuracy on subtask B2. A
large difference in accuracy on development data can be observed with
relatively minor changes in our network's architecture and hyperparameters. We
therefore expect fine-tuning of these parameters to yield higher accuracies.

使用深度残差网络和字节表示，仅在已发布的数据集上进行训练，可以对相似语言进行有效区分，获得了高精度的分类结果。