We present a simple document alignment method that incorporates sentence
order information in both candidate generation and candidate re-scoring. Our
method results in 61% relative reduction in error compared to the best
previously published result on the WMT16 document alignment shared task. Our
method improves downstream MT performance on web-scraped Sinhala--English
documents from ParaCrawl, outperforming the document alignment method used in
the most recent ParaCrawl release. It also outperforms a comparable corpora
method which uses the same multilingual embeddings, demonstrating that
exploiting sentence order is beneficial even if the end goal is sentence-level
bitext.

该研究提出了一种简单的文档对齐方法，该方法在候选生成和候选重新打分中都利用了句子顺序信息，并相对于 WMT16 文档对齐任务的最佳结果减少了 61％的错误。该方法提高了从 ParaCrawl 的网页抓取的僧伽罗语 - 英语文档的下游机器翻译性能，并且优于最新 ParaCrawl 发布中使用的文档对齐方法。 此外，该方法还优于使用相同多语言嵌入的可比语料库方法，证明利用句子顺序即使目标是句子级双语文本也是有益的。

利用句子顺序进行文档对齐

Exploiting Sentence Order in Document Alignment

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。