Many machine translation models are trained on bilingual corpus, which
consist of aligned sentence pairs from two different languages with same
semantic. However, there is a qualitative discrepancy between train and test
set in bilingual corpus. While the most train sentences are created via
automatic techniques such as crawling and sentence-alignment methods, the test
sentences are annotated with the consideration of fluency by human. We suppose
this discrepancy in training corpus will yield performance drop of translation
model. In this work, we define \textit{fluency noise} to determine which parts
of train sentences cause them to seem unnatural. We show that \textit{fluency
noise} can be detected by simple gradient-based method with pre-trained
classifier. By removing \textit{fluency noise} in train sentences, our final
model outperforms the baseline on WMT-14 DE$\rightarrow$EN and
RU$\rightarrow$EN. We also show the compatibility with back-translation
augmentation, which has been commonly used to improve the fluency of the
translation model. At last, the qualitative analysis of \textit{fluency noise}
provides the insight of what points we should focus on.

本文描述了一种基于梯度的机器翻译模型训练方法，在去除了语言偏差和提高流畅度的基础上，使用反转和增强技术提高翻译模型的性能。