In this work we introduce dual conditional cross-entropy filtering for noisy
parallel data. For each sentence pair of the noisy parallel corpus we compute
cross-entropy scores according to two inverse translation models trained on
clean data. We penalize divergent cross-entropies and weigh the penalty by the
cross-entropy average of both models. Sorting or thresholding according to
these scores results in better subsets of parallel data. We achieve higher BLEU
scores with models trained on parallel data filtered only from Paracrawl than
with models trained on clean WMT data. We further evaluate our method in the
context of the WMT2018 shared task on parallel corpus filtering and achieve the
overall highest ranking scores of the shared task, scoring top in three out of
four subtasks.

通过两个反向翻译模型计算每个句子对于嘈杂平行语料库的交叉熵分数，对分歧的交叉熵进行惩罚并按两个模型的交叉熵平均值加权。按这些分数排序或分阈值处理可得到更好的平行数据子集，该方法在去噪过程中实现了更高 BLEU 得分，并在 WMT2018 共享任务中获得了最高排名得分。

噪声平行语料的双重条件交叉熵滤波

Dual Conditional Cross-Entropy Filtering of Noisy Parallel Corpora

This paper describes the Microsoft submission to the WMT2018 news translation
shared task. We participated in one language direction -- English-German. Our
system follows current best-practice and combines state-of-the-art models with
new data filtering (dual conditional cross-entropy filtering) and sentence
weighting methods. We trained fairly standard Transformer-big models with an
updated version of Edinburgh's training scheme for WMT2017 and experimented
with different filtering schemes for Paracrawl. According to automatic metrics
(BLEU) we reached the highest score for this subtask with a nearly 2 BLEU point
margin over the next strongest system. Based on human evaluation we ranked
first among constrained systems. We believe this is mostly caused by our data
filtering/weighting regime.

本文介绍了微软参加 WMT2018 新闻翻译共享任务的提交，对于英语到德语的一种语言方向中，基于最佳实践，采用了最先进的模型和新数据过滤和句子加权方法来训练在 Paracrawl 上实验结果表明，根据自动指标 (BLEU)，我们在此子任务中得分最高，比接下来最强大的系统高近 2 BLEU 分数，根据人工评估，我们在受限系统中排名第一，我们认为这主要是由于我们的数据过滤 / 加权组合的实施。