Recovering document annotations for sentence-level bitext

Rachel Wicks, Matt Post, Philipp Koehn

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.
Anthology ID:
2024.findings-acl.589
Volume:
Findings of the Association for Computational Linguistics ACL 2024
Month:
August
Year:
2024
Address:
Bangkok, Thailand and virtual meeting
Editors:
Lun-Wei Ku, Andre Martins, Vivek Srikumar
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
9876–9890
Language:
URL:
https://aclanthology.org/2024.findings-acl.589
DOI:
Bibkey:
Cite (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.
Cite (Informal):
Recovering document annotations for sentence-level bitext (Wicks et al., Findings 2024)
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PDF:
https://preview.aclanthology.org/nschneid-patch-4/2024.findings-acl.589.pdf
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