More than Just Statistical Recurrence: Human and Machine Unsupervised
  Learning of Māori Word Segmentation across Morphological Processes
Ashvini Varatharaj Simon Todd
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.