GlotCC: An Open Broad-Coverage CommonCrawl Corpus and Pipeline for Minority Languages
This addresses the problem of data scarcity for minority languages in NLP research, enabling broader language model development, though it is incremental as it builds on existing corpus creation methods.
The authors tackled the lack of large, clean text corpora for minority languages by presenting GlotCC, a 2TB general domain corpus derived from CommonCrawl that covers over 1000 languages, along with an open-source pipeline for reproducible generation.
The need for large text corpora has increased with the advent of pretrained language models and, in particular, the discovery of scaling laws for these models. Most available corpora have sufficient data only for languages with large dominant communities. However, there is no corpus available that (i) covers a wide range of minority languages; (ii) is generated by an open-source reproducible pipeline; and (iii) is rigorously cleaned from noise, making it trustworthy to use. We present GlotCC, a clean, document-level, 2TB general domain corpus derived from CommonCrawl, covering more than 1000 languages. We make GlotCC and the system used to generate it - including the pipeline, language identification model, and filters - available to the research community. Corpus v. 1.0 https://huggingface.co/datasets/cis-lmu/GlotCC-v1, Pipeline v. 3.0 https://github.com/cisnlp/GlotCC.