SampoNLP: A Self-Referential Toolkit for Morphological Analysis of Subword Tokenizers

TL;DR

SampoNLP is a novel toolkit that creates morphological lexicons for Uralic languages without relying on existing resources, enabling systematic evaluation of subword tokenizers and providing practical vocabulary size recommendations.

Contribution

It introduces SampoNLP, a corpus-free, MDL-inspired toolkit for morphological lexicon creation tailored for low-resource, morphologically rich languages, and proposes a new metric for tokenizer evaluation.

Findings

High-purity lexicons for Finnish, Hungarian, and Estonian generated.

Optimal vocabulary sizes identified for BPE tokenizers in these languages.

Demonstrates limitations of standard BPE for agglutinative languages.

Abstract

The quality of subword tokenization is critical for Large Language Models, yet evaluating tokenizers for morphologically rich Uralic languages is hampered by the lack of clean morpheme lexicons. We introduce SampoNLP, a corpus-free toolkit for morphological lexicon creation using MDL-inspired Self-Referential Atomicity Scoring, which filters composite forms through internal structural cues - suited for low-resource settings. Using the high-purity lexicons generated by SampoNLP for Finnish, Hungarian, and Estonian, we conduct a systematic evaluation of BPE tokenizers across a range of vocabulary sizes (8k-256k). We propose a unified metric, the Integrated Performance Score (IPS), to navigate the trade-off between morpheme coverage and over-splitting. By analyzing the IPS curves, we identify the "elbow points" of diminishing returns and provide the first empirically grounded recommendations for optimal vocabulary sizes (k) in these languages. Our study not only offers practical guidance but also quantitatively demonstrates the limitations of standard BPE for highly agglutinative languages. The SampoNLP library and all generated resources are made publicly available: https://github.com/AragonerUA/SampoNLP