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        <title>Recovering document annotations for sentence-level bitext</title>
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    <abstract>In machine translation, historical models were incapable of handling longer contexts, so the lack of document-level datasets was less noticeable. Now, despite the emergence of long-sequence methods, we remain within a sentence-level paradigm and without data to adequately approach context-aware machine translation. Most large-scale datasets have been processed through a pipeline that discards document-level metadata. In this work, we reconstruct document-level information for three (ParaCrawl, News Commentary, and Europarl) large datasets in German, French, Spanish, Italian, Polish, and Portuguese (paired with English). We then introduce a document-level filtering technique as an alternative to traditional bitext filtering. We present this filtering with analysis to show that this method prefers context-consistent translations rather than those that may have been sentence-level machine translated. Last we train models on these longer contexts and demonstrate improvement in document-level translation without degradation of sentence-level translation. We release our dataset, ParaDocs, and resulting models as a resource to the community.</abstract>
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%0 Conference Proceedings
%T Recovering document annotations for sentence-level bitext
%A Wicks, Rachel
%A Post, Matt
%A Koehn, Philipp
%Y Ku, Lun-Wei
%Y Martins, Andre
%Y Srikumar, Vivek
%S Findings of the Association for Computational Linguistics ACL 2024
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand and virtual meeting
%F wicks-etal-2024-recovering
%X In machine translation, historical models were incapable of handling longer contexts, so the lack of document-level datasets was less noticeable. Now, despite the emergence of long-sequence methods, we remain within a sentence-level paradigm and without data to adequately approach context-aware machine translation. Most large-scale datasets have been processed through a pipeline that discards document-level metadata. In this work, we reconstruct document-level information for three (ParaCrawl, News Commentary, and Europarl) large datasets in German, French, Spanish, Italian, Polish, and Portuguese (paired with English). We then introduce a document-level filtering technique as an alternative to traditional bitext filtering. We present this filtering with analysis to show that this method prefers context-consistent translations rather than those that may have been sentence-level machine translated. Last we train models on these longer contexts and demonstrate improvement in document-level translation without degradation of sentence-level translation. We release our dataset, ParaDocs, and resulting models as a resource to the community.
%U https://aclanthology.org/2024.findings-acl.589
%P 9876-9890

Markdown (Informal)

[Recovering document annotations for sentence-level bitext](https://aclanthology.org/2024.findings-acl.589) (Wicks et al., Findings 2024)

• Recovering document annotations for sentence-level bitext (Wicks et al., Findings 2024)

ACL

• Rachel Wicks, Matt Post, and Philipp Koehn. 2024. Recovering document annotations for sentence-level bitext. In Findings of the Association for Computational Linguistics ACL 2024, pages 9876–9890, Bangkok, Thailand and virtual meeting. Association for Computational Linguistics.