In this work, we exploit the simple idea that a document and its translation should contain approximately the same information , in approximately the same order. We propose methods for both document pair candidate generation and candidate re-scoring which incorporate high-level order information . Our method results in 61\% relative reduction in error versus the best previously published result on the WMT16 document alignment shared task. We also apply our method to web-scraped Sinhala-English documents from ParaCrawl and find that our method improves MT performance by 1.2 BLEU over the current ParaCrawl document alignment method  .
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 .
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In this work, we exploit the simple idea that a document and its translation should contain approximately the same information , in approximately the same order. We propose methods for both document pair
We present a simple document alignment method that incorporates sentence order information in both
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which incorporate high-level order information
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versus
compared to
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We also apply our method to
Our method improves downstream MT performance on
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Sinhala-English
Sinhala--English
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and find that our method improves MT performance by 1.2 BLEU over the current ParaCrawl
, outperforming the
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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