# Annotating and Inferring Compositional Structures in Numeral Systems Across Languages

arXiv: https://arxiv.org/abs/2503.01625 · HTML: https://mlsanity.com/paper/2503.01625/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 59 references that are in the map.
- First posted on arXiv 3 Mar 2025.

## Builds on (most cited first, 3 of 3)

- [Neural Machine Translation of Rare Words with Subword Units](https://mlsanity.com/paper/1508.07909/) (2015)
- [Subword Regularization: Improving Neural Network Translation Models with Multiple Subword Candidates](https://mlsanity.com/paper/1804.10959/) (2018)
- [What do complexity measures measure? Correlating and validating corpus-based measures of morphological complexity](https://mlsanity.com/paper/2204.05056/) (2022)

## Built on by (most cited first, 2 of 2)

- [Complexity counts: global and local perspectives on Indo-Aryan numeral systems](https://mlsanity.com/paper/2505.21510/) (2025)
- [How communicatively optimal are exact numeral systems? Once more on lexicon size and morphosyntactic complexity](https://mlsanity.com/paper/2602.20372/) (2026)

## Abstract (arXiv)

Numeral systems across the world's languages vary in fascinating ways, both regarding their synchronic structure and the diachronic processes that determined how they evolved in their current shape. For a proper comparison of numeral systems across different languages, however, it is important to code them in a standardized form that allows for the comparison of basic properties. Here, we present a simple but effective coding scheme for numeral annotation, along with a workflow that helps to code numeral systems in a computer-assisted manner, providing sample data for numerals from 1 to 40 in 25 typologically diverse languages. We perform a thorough analysis of the sample, focusing on the systematic comparison between the underlying and the surface morphological structure. We further experiment with automated models for morpheme segmentation, where we find allomorphy as the major reason for segmentation errors. Finally, we show that subword tokenization algorithms are not viable for discovering morphemes in low-resource scenarios.