  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.
