One of the vital breakthroughs in the history of machine translation is the
development of the Transformer model. Not only it is revolutionary for various
translation tasks, but also for a majority of other NLP tasks. In this paper,
we aim at a Transformer-based system that is able to translate a source
sentence in German to its counterpart target sentence in English. We perform
the experiments on the news commentary German-English parallel sentences from
the WMT'13 dataset. In addition, we investigate the effect of the inclusion of
additional general-domain data in training from the IWSLT'16 dataset to improve
the Transformer model performance. We find that including the IWSLT'16 dataset
in training helps achieve a gain of 2 BLEU score points on the test set of the
WMT'13 dataset. Qualitative analysis is introduced to analyze how the usage of
general-domain data helps improve the quality of the produced translation
sentences.

本文介绍了一种基于 Transformer 模型的系统，旨在将德语源句子翻译成其英语目标句子，通过 WMT'13 数据集上的新闻评论德英平行句子的实验，我们发现在训练中加入 IWSLT'16 数据集的泛领域数据可以提高 Transformer 模型性能的 2 个 BLEU 分数点，从而改善生成翻译句子的质量。