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.- Anthology ID:
- 2024.acl-long.712
- Volume:
- Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Month:
- August
- Year:
- 2024
- Address:
- Bangkok, Thailand
- Editors:
- Lun-Wei Ku, Andre Martins, Vivek Srikumar
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 13185–13197
- Language:
- URL:
- https://preview.aclanthology.org/icon-24-ingestion/2024.acl-long.712/
- DOI:
- 10.18653/v1/2024.acl-long.712
- Cite (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.
- Cite (Informal):
- Document-Level Machine Translation with Large-Scale Public Parallel Corpora (Pal et al., ACL 2024)
- PDF:
- https://preview.aclanthology.org/icon-24-ingestion/2024.acl-long.712.pdf