Evaluating Morphological Plausibility of Subword Tokenization via Statistical Alignment with Morpho-Syntactic Features

ACL ARR 2025 May Submission4027 Authors

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.

Paper Type: Short

Research Area: Phonology, Morphology and Word Segmentation

Research Area Keywords: morphological segmentation,subword representations,morphological analysis

Contribution Types: NLP engineering experiment, Approaches to low-resource settings

Languages Studied: Armenian,Czech,Dutch,English,Finnish,German,Kannada,Serbo-Croatian,Slovak

Submission Number: 4027