We present a novel metric for the evaluation of the 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.

翻译：本文提出了一种评估子词切分形态合理性的新颖度量方法。与通常需要黄金切分数据（该数据对于许多语言要么无法获取，要么质量参差不齐）的词素边界或检索F值不同，我们的方法利用了形态句法特征。这些特征在Universal Dependencies或UniMorph等资源中可用于更广泛的语言。该度量通过IBM Model 1概率性地将子词与形态特征进行对齐。实验表明，该度量与传统词素边界召回率具有良好相关性，同时能更广泛地适用于具有不同形态系统的语言。