A Shocking Amount of the Web is Machine Translated: Insights from Multi-Way Parallelism

Brian Thompson, Mehak Dhaliwal, Peter Frisch, Tobias Domhan, Marcello Federico


Abstract
We show that content on the web is often translated into many languages, and the low quality of these multi-way translations indicates they were likely created using Machine Translation (MT). Multi-way parallel, machine generated content not only dominates the translations in lower resource languages; it also constitutes a large fraction of the total web content in those languages. We also find evidence of a selection bias in the type of content which is translated into many languages, consistent with low quality English content being translated en masse into many lower resource languages, via MT. Our work raises serious concerns about training models such as multilingual large language models on both monolingual and bilingual data scraped from the web.
Anthology ID:
2024.findings-acl.103
Volume:
Findings of the Association for Computational Linguistics: ACL 2024
Month:
August
Year:
2024
Address:
Bangkok, Thailand
Editors:
Lun-Wei Ku, Andre Martins, Vivek Srikumar
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1763–1775
Language:
URL:
https://preview.aclanthology.org/build-pipeline-with-new-library/2024.findings-acl.103/
DOI:
10.18653/v1/2024.findings-acl.103
Bibkey:
Cite (ACL):
Brian Thompson, Mehak Dhaliwal, Peter Frisch, Tobias Domhan, and Marcello Federico. 2024. A Shocking Amount of the Web is Machine Translated: Insights from Multi-Way Parallelism. In Findings of the Association for Computational Linguistics: ACL 2024, pages 1763–1775, Bangkok, Thailand. Association for Computational Linguistics.
Cite (Informal):
A Shocking Amount of the Web is Machine Translated: Insights from Multi-Way Parallelism (Thompson et al., Findings 2024)
Copy Citation:
PDF:
https://preview.aclanthology.org/build-pipeline-with-new-library/2024.findings-acl.103.pdf