Does Corpus Quality Really Matter for Low-Resource Languages?
This work addresses the problem of data quality for low-resource language processing, suggesting it may be less critical than assumed, but it is incremental as it builds on prior critiques of multilingual corpora.
The study investigated whether corpus quality significantly impacts downstream natural language understanding (NLU) performance for low-resource languages, using Basque as a case study, and found that despite a new high-quality corpus (EusCrawl) having 66% high-quality documents compared to <33% in existing corpora, NLU task results were similar across corpora.
The vast majority of non-English corpora are derived from automatically filtered versions of CommonCrawl. While prior work has identified major issues on the quality of these datasets (Kreutzer et al., 2021), it is not clear how this impacts downstream performance. Taking representation learning in Basque as a case study, we explore tailored crawling (manually identifying and scraping websites with high-quality content) as an alternative to filtering CommonCrawl. Our new corpus, called EusCrawl, is similar in size to the Basque portion of popular multilingual corpora like CC100 and mC4, yet it has a much higher quality according to native annotators. For instance, 66% of documents are rated as high-quality for EusCrawl, in contrast with <33% for both mC4 and CC100. Nevertheless, we obtain similar results on downstream NLU tasks regardless of the corpus used for pre-training. Our work suggests that NLU performance in low-resource languages is not primarily constrained by the quality of the data, and other factors like corpus size and domain coverage can play a more important role.