@inproceedings{coleman-etal-2026-ran,
    title = "{RAN}: Resource Abundance Notation for Languages in {NLP}",
    author = "Coleman, Jared  and
      Coleman, Tain{\~a}  and
      Krishnmachari, Bhaskar",
    editor = "Mager, Manuel  and
      Ebrahimi, Abteen  and
      Bui, Minh Duc  and
      Pugh, Robert  and
      Oncevay, Arturo  and
      Chiruzzo, Luis  and
      Solano, Rolando Coto  and
      Rijhwani, Shruti  and
      Von Der Wense, Katharina",
    booktitle = "Proceedings of the Sixth Workshop on {NLP} for Indigenous Languages of the {A}mericas ({A}mericas{NLP})",
    month = jul,
    year = "2026",
    address = "San Diego, California, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl-workshops/2026.americasnlp-6.15/",
    pages = "168--172",
    ISBN = "979-8-89176-415-6",
    abstract = "The term ``low-resource'' is used pervasively in NLP but communicates almost nothing precise. We propose RAN (Resource Abundance Notation), a compact, multi-dimensional notation for quantifying a language{'}s NLP resource profile. A RAN score is written as S/M/L{\_}1-B{\_}1/L{\_}2-B{\_}2/..., where S = floor(log10(speakers)), M = floor(log10(monolingual sentences)), and each L{\_}i-B{\_}i pair records a bilingual partner and floor(log10(parallel sentences)). Values derive from canonical sources: Wikidata for speakers, OSCAR 23.01 for monolingual corpora, and (where available) OPUS for parallel corpora. We score 20 typologically diverse languages and correlate each profile against published benchmarks for three tasks: machine translation (MT, via NLLB-200 chrF++), named entity recognition (NER, via XTREME XLM-R WikiANN F1), and part-of-speech tagging (POS, via XTREME XLM-R UD accuracy). The RAN components carry complementary information: a linear model using all three explains 52{\%} of MT variance, 76{\%} of NER variance, and 72{\%} of POS variance. Among single predictors, B{\_}max (the largest bilingual corpus, regardless of partner) is strongest for the cross-lingual transfer tasks (NER, POS), while M and B{\_}en are strongest for MT. RAN is designed first as a communication tool, not a predictive model."
}