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This paper addresses the critical challenge of tokenization alignment in low-resource languages by introducing MoirfEolas, a dataset mapping Irish words to their morphological components. The study evaluates various algorithms against this resource and proposes CríochScore as a metric to quantify how well tokenizers respect linguistic boundaries, revealing that unigram models currently offer superior morphological alignment for Irish. These findings provide essential technical insights for advancing natural language processing in linguistically complex environments beyond just the Irish language.
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MoirfEolas and Cr\'iochScore: Developing Resources for and the Evaluation of Tokenization Alignment with Irish Morphology

arXiv2 min read

arXiv:2609.05022v1 Announce Type: new Abstract: This paper presents new tokenization resources for Irish and evaluation measures of alignment with the morphological boundaries of the language. We present MoirfEolas, a dataset of over 35,000 Irish words mapped to their respective eclipses, prefixes and suffixes as well as an evaluation metric Cr\'iochScore, that evaluates the alignment of tokenizations with the morphological boundaries present in MoirfEolas. We evaluate common tokenization algorithms using Cr\'iochScore as well as intrinsic metrics present in the tokenization literature. We find that the Unigram Language Model aligns with Irish morphology more often than the other algorithms evaluated. We also find trade-offs between morphological-alignment of tokenization with both compression as well as vocabulary efficiency, providing practical insights for Irish natural language processing development. This dataset contributes towards combating the Irish language's low-resource status; moreover, the construction process reported in this paper can be emulated by other languages to create specialised morphological resources.

Abstract:This paper presents new tokenization resources for Irish and evaluation measures of alignment with the morphological boundaries of the language. We present MoirfEolas, a dataset of over 35,000 Irish words mapped to their respective eclipses, prefixes and suffixes as well as an evaluation metric CríochScore, that evaluates the alignment of tokenizations with the morphological boundaries present in MoirfEolas. We evaluate common tokenization algorithms using CríochScore as well as intrinsic metrics present in the tokenization literature. We find that the Unigram Language Model aligns with Irish morphology more often than the other algorithms evaluated. We also find trade-offs between morphological-alignment of tokenization with both compression as well as vocabulary efficiency, providing practical insights for Irish natural language processing development. This dataset contributes towards combating the Irish language's low-resource status; moreover, the construction process reported in this paper can be emulated by other languages to create specialised morphological resources.

Computation and Language (cs.CL)

arXiv:2609.05022 [cs.CL]

(or arXiv:2609.05022v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2609.05022 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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