Resource Abundance Notation
RAN (Resource Abundance Notation) is a compact, multi-dimensional, reproducible way to communicate a language's NLP resource profile at a glance.
The term "low-resource" is used constantly in NLP but means almost nothing precise. A paper might call a language low-resource whether it has 500 parallel sentences or 500,000, whether there are 10 fluent speakers or 10 million. Coarse single-number classifications (e.g. Joshi et al. 2020's classes 0–5) don't capture that spread. RAN gives a short score a reader can decode without a lookup table.
The score
A RAN score is written as a fixed sequence of slash-separated components:
S / M / L1-B1 / L2-B2 / …
- S = ⌊log₁₀(fluent speakers)⌋ — kept for what it says about a language's vitality and community, even though it doesn't predict system performance.
- M = ⌊log₁₀(monolingual corpus sentences)⌋.
- each Li-Bi = a bilingual partner language
Liand ⌊log₁₀(parallel sentences with it)⌋, listed in descending order of Bi so the strongest resource comes first. A trailing slash with nothing after it (e.g.0/3/) means no parallel corpus at all.
Every digit is an order of magnitude, so the numbers read directly:
| Digit | Magnitude |
|---|---|
| 0 | ones |
| 1 | tens |
| 2 | hundreds |
| 3 | thousands |
| 4 | tens of thousands |
| 5 | hundreds of thousands |
| 6 | millions |
| 7 | tens of millions |
| 8 | hundreds of millions |
| 9 | billions |
How to read it
| Language | RAN | Reading |
|---|---|---|
| Spanish | 8/9/en-8/fr-8/pt-8 | Hundreds of millions of speakers, billions of monolingual sentences, hundreds of millions of parallel sentences each with English, French, and Portuguese |
| Swahili | 7/4/en-7/fr-6 | Tens of millions of speakers, tens of thousands of monolingual sentences, tens of millions of en–sw parallel sentences |
| Quechua | 6/0/en-6/es-2 | Millions of speakers, almost no monolingual corpus, millions of en–qu parallel sentences |
| Cherokee | 4/2/en-4 | Tens of thousands of speakers, hundreds of monolingual sentences, tens of thousands of en–chr parallel sentences |
| Owens Valley Paiute | 0/3/en-3 | Less than ten fluent speakers, thousands of monolingual sentences, thousands of parallel sentences (the Kubishi Dictionary) |
Why it's built this way
RAN is designed first as a communication tool. It is:
- Compact: fits in an abstract, a slide title, or a table cell.
- Multi-dimensional: separates speaker count, monolingual data, and bilingual data, which behave very differently.
- Reproducible: apply the same rule and sources and you get the same score.
- Interpretable: the digits are orders of magnitude.
The bilingual components also turn a set of languages into a graph — partners are edges, weighted by parallel-corpus size — which makes pivot paths explicit. Quechua, for instance, has far more parallel text with Spanish than with English, so Quechua→Spanish→English can beat going direct. Recording the largest partner rather than fixing English as the pivot is deliberate: the RAN Paper finds the best bilingual partner is the strongest predictor of cross-lingual transfer.
How scores are computed
While RAN does not prescribe which source you draw counts from, a score is only as reliable as its source. In the sample provided here, we keep scores comparable by using one consistent set of sources across every language, and each language page states its raw counts and where they came from:
| Component | Source | What is counted |
|---|---|---|
| S (speakers) | Ethnologue | Fluent (L1) speakers |
| M (monolingual) | OSCAR 23.01 | Sentences in the largest monolingual corpus |
| Li-Bi (bilingual) | OPUS | Sentences in the largest parallel corpus with partner Li |
These are sensible defaults, not a mandate. Corpora grow, so a score is implicitly as of its sources.
Owens Valley Paiute isn't covered by OSCAR or OPUS, so its monolingual and bilingual counts come from community documentation (the Kubishi Dictionary) and its speaker count is a community estimate rather than an Ethnologue figure.
Explore
- Languages: a sample of scored languages, sortable by component — illustrative, not a definitive registry. Start here to browse.
- The RAN paper: the full write-up, including how the components correlate with benchmark performance across MT, NER, and POS.
- Contributing: propose a language for the sample, or correct a score.
RAN comes from the Kubishi Research Group at Loyola Marymount University (PI: Jared Coleman) and is described in a short paper to appear at AmericasNLP 2026. The sample deliberately includes Indigenous languages of the Americas (Quechua, Guarani, Cherokee, and Owens Valley Paiute) that are routinely bundled under one "low-resource" label despite differing by orders of magnitude.