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。

使用原始 Transformer 对机器翻译任务进行有效的通用领域数据收录

Effective General-Domain Data Inclusion for the Machine Translation Task by Vanilla Transformers

We detect out-of-training-distribution sentences in Neural Machine
Translation using the Bayesian Deep Learning equivalent of Transformer models.
For this we develop a new measure of uncertainty designed specifically for long
sequences of discrete random variables -- i.e. words in the output sentence.
Our new measure of uncertainty solves a major intractability in the naive
application of existing approaches on long sentences. We use our new measure on
a Transformer model trained with dropout approximate inference. On the task of
German-English translation using WMT13 and Europarl, we show that with dropout
uncertainty our measure is able to identify when Dutch source sentences,
sentences which use the same word types as German, are given to the model
instead of German.

使用等效于 Transformer 模型的贝叶斯深度学习方法检测神经机器翻译中的训练数据分布外句子。我们使用长序列离散随机变量的新不确定性衡量法解决了现有方法在长句子上不适用的问题，并在使用 dropout 的 Transformer 模型上执行德语 - 英语翻译任务，证明我们的方法能够当 Dutch 源句子输入时区分其与德语句子。