Non-M\=aori-speaking New Zealanders (NMS)are able to segment M\=aori words in
a highlysimilar way to fluent speakers (Panther et al.,2024). This ability is
assumed to derive through the identification and extraction of statistically
recurrent forms. We examine this assumption by asking how NMS segmentations
compare to those produced by Morfessor, an unsupervised machine learning model
that operates based on statistical recurrence, across words formed by a variety
of morphological processes. Both NMS and Morfessor succeed in segmenting words
formed by concatenative processes (compounding and affixation without
allomorphy), but NMS also succeed for words that invoke templates
(reduplication and allomorphy) and other cues to morphological structure,
implying that their learning process is sensitive to more than just statistical
recurrence.

非毛利族新西兰人（NMS）在对毛利语的词汇分割方面与母语者表现高度相似，这种能力假设是通过识别和提取统计重复形式而获得的。我们通过比较 NMS 的分割结果与 Morfessor 的结果（一种基于统计重复的无监督机器学习模型）来验证这一假设，结果表明 NMS 在形态学过程中成功分割了通过合并过程（如复合词和无音位词缀）形成的词汇，同时他们的学习过程对于通过模板和其他形态学结构线索形成的词汇也敏感，说明 NMS 的学习过程不仅仅依赖于统计重复。