A Shocking Amount of the Web is Machine Translated: Insights from
  Multi-Way Parallelism
Brian Thompson Mehak Preet Dhaliwal Peter Frisch Tobias Domhan Marcello Federico
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.