  Data quality is a problem that perpetually resurfaces throughout the field of
NLP, regardless of task, domain, or architecture, and remains especially severe
for lower-resource languages. A typical and insidious issue, affecting both
training data and model output, is data that is repetitive and dominated by
linguistically uninteresting boilerplate, such as price catalogs or
computer-generated log files. Though this problem permeates many web-scraped
corpora, there has yet to be a benchmark to test against, or a systematic study
to find simple metrics that generalize across languages and agree with human
judgements of data quality. In the present work, we create and release BREAD, a
human-labeled benchmark on repetitive boilerplate vs. plausible linguistic
content, spanning 360 languages. We release several baseline CRED (Character
REDundancy) scores along with it, and evaluate their effectiveness on BREAD. We
hope that the community will use this resource to develop better filtering
methods, and that our reference implementations of CRED scores can become
standard corpus evaluation tools, driving the development of cleaner language
modeling corpora, especially in low-resource languages.
