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        <title>Document-Level Machine Translation with Large-Scale Public Parallel Corpora</title>
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    <abstract>Despite the fact that document-level machine translation has inherent advantages over sentence-level machine translation due to additional information available to a model from document context, most translation systems continue to operate at a sentence level. This is primarily due to the severe lack of publicly available large-scale parallel corpora at the document level. We release a large-scale open parallel corpus with document context extracted from ParaCrawl in five language pairs, along with code to compile document-level datasets for any language pair supported by ParaCrawl. We train context-aware models on these datasets and find improvements in terms of overall translation quality and targeted document-level phenomena. We also analyse how much long-range information is useful to model some of these discourse phenomena and find models are able to utilise context from several preceding sentences.</abstract>
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%0 Conference Proceedings
%T Document-Level Machine Translation with Large-Scale Public Parallel Corpora
%A Pal, Proyag
%A Birch, Alexandra
%A Heafield, Kenneth
%Y Ku, Lun-Wei
%Y Martins, Andre
%Y Srikumar, Vivek
%S Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand
%F pal-etal-2024-document
%X Despite the fact that document-level machine translation has inherent advantages over sentence-level machine translation due to additional information available to a model from document context, most translation systems continue to operate at a sentence level. This is primarily due to the severe lack of publicly available large-scale parallel corpora at the document level. We release a large-scale open parallel corpus with document context extracted from ParaCrawl in five language pairs, along with code to compile document-level datasets for any language pair supported by ParaCrawl. We train context-aware models on these datasets and find improvements in terms of overall translation quality and targeted document-level phenomena. We also analyse how much long-range information is useful to model some of these discourse phenomena and find models are able to utilise context from several preceding sentences.
%R 10.18653/v1/2024.acl-long.712
%U https://aclanthology.org/2024.luhme-long.712/
%U https://doi.org/10.18653/v1/2024.acl-long.712
%P 13185-13197

Markdown (Informal)

[Document-Level Machine Translation with Large-Scale Public Parallel Corpora](https://aclanthology.org/2024.luhme-long.712/) (Pal et al., ACL 2024)

• Document-Level Machine Translation with Large-Scale Public Parallel Corpora (Pal et al., ACL 2024)

ACL

• Proyag Pal, Alexandra Birch, and Kenneth Heafield. 2024. Document-Level Machine Translation with Large-Scale Public Parallel Corpora. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 13185–13197, Bangkok, Thailand. Association for Computational Linguistics.