More than Just Statistical Recurrence: Human and Machine Unsupervised Learning of Māori Word Segmentation across Morphological Processes

Ashvini Varatharaj, Simon Todd
3/21/2024
cs.CL

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Abstract

Non-Māori-speaking New Zealanders (NMS)are able to segment Māori 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.

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Cite this paper

@article{varatharaj2024more,
  title  = {More than Just Statistical Recurrence: Human and Machine Unsupervised Learning of Māori Word Segmentation across Morphological Processes},
  author = {Ashvini Varatharaj and Simon Todd},
  year   = {2024},
  eprint = {2403.14444},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2403.14444v1}
}

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