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        <title>Evaluating Morphological Plausibility of Subword Tokenization via Statistical Alignment with Morpho-Syntactic Features</title>
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    <abstract>We present a novel metric for the evaluation of morphological plausibility of subword segmentation.Unlike the typically used morpheme boundary or retrieval F-score, which requires gold segmentation data that is either unavailable or of inconsistent quality across many languages, our approach utilizes morpho-syntactic features.These are available in resources such as Universal Dependencies or UniMorph for a much wider range of languages.The metric works by probabilistically aligning subwords with morphological features through an IBM Model 1.Our experiments show that the metric correlates well with traditional morpheme boundary recall while being more broadly applicable across languages with different morphological systems.</abstract>
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%0 Conference Proceedings
%T Evaluating Morphological Plausibility of Subword Tokenization via Statistical Alignment with Morpho-Syntactic Features
%A Stephen, Abishek
%A Libovický, Jindřich
%Y Demberg, Vera
%Y Inui, Kentaro
%Y Marquez, Lluís
%S Findings of the Association for Computational Linguistics: EACL 2026
%D 2026
%8 March
%I Association for Computational Linguistics
%C Rabat, Morocco
%@ 979-8-89176-386-9
%F stephen-libovicky-2026-evaluating
%X We present a novel metric for the evaluation of morphological plausibility of subword segmentation.Unlike the typically used morpheme boundary or retrieval F-score, which requires gold segmentation data that is either unavailable or of inconsistent quality across many languages, our approach utilizes morpho-syntactic features.These are available in resources such as Universal Dependencies or UniMorph for a much wider range of languages.The metric works by probabilistically aligning subwords with morphological features through an IBM Model 1.Our experiments show that the metric correlates well with traditional morpheme boundary recall while being more broadly applicable across languages with different morphological systems.
%U https://aclanthology.org/2026.findings-eacl.196/
%P 3783-3791

Markdown (Informal)

[Evaluating Morphological Plausibility of Subword Tokenization via Statistical Alignment with Morpho-Syntactic Features](https://aclanthology.org/2026.findings-eacl.196/) (Stephen & Libovický, Findings 2026)

• Evaluating Morphological Plausibility of Subword Tokenization via Statistical Alignment with Morpho-Syntactic Features (Stephen & Libovický, Findings 2026)

ACL

• Abishek Stephen and Jindřich Libovický. 2026. Evaluating Morphological Plausibility of Subword Tokenization via Statistical Alignment with Morpho-Syntactic Features. In Findings of the Association for Computational Linguistics: EACL 2026, pages 3783–3791, Rabat, Morocco. Association for Computational Linguistics.