More than Just Statistical Recurrence: Human and Machine Unsupervised Learning of M āori Word Segmentation across Morphological Processes (2024)

Abstract: Non-Māori-speaking New Zealanders (NMS) are able to segment Māori words in a highly similar 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.

## introduction humans have a powerful ability to build implicit linguistic knowledge incidentally, based on passive processes that identify and extract statistically recurrent patterns @xcite @xcite . for example, new zealanders who are regularly ambiently exposed to māori, but do not speak it, nevertheless have māori lexical and phonotactic knowledge @xcite and can morphologically segment māori words at above-chance levels @xcite . these findings imply that regular exposure to a language yields a proto-lexicon: an implicit memory-store of forms that recur with statistical regularity in the language, including both words and word-parts @xcite . in this paper, we are concerned with the way that the proto-lexicon is constructed, and the way that its construction interacts with language structure. we examine the extent to which the ability of non-māori-speaking new zealanders (nms) to morphologically segment māori words is explained by naive statistical learning, in which their protolexicon is assumed to be formed purely through the identification and extraction of statistically recurrent forms in ambient māori. to do so, we generate expectations for what morphological segmentation would look like through naive statistical learning processes from morfessor @xcite , an unsupervised bayesian segmentation model. we compare the segmentations produced by morfessor to those produced by nms and examine how they vary across words formed by different morphological processes. through two analyses, we argue that nms do more than a naive statistical learning model would suggest. first, we compare the segmentations of morfessor and nms across māori words formed by affixation and compounding, both concatenative processes, and words formed by reduplication, a templatic process. we find that both are accurate on words formed by affixation and compounding, but nms are more accurate on words formed by reduplication, suggesting that nms identify and extract both statistically recurrent forms and higher-level abstract templates. then, zooming in on words formed by concatenative processes, we ask whether there are other cues to morphological structure that nms may be picking up on, such as vowel length. we compare the performance of morfessor across real māori words that may contain such cues and constructed words that have the same statistical properties but lack any reliable alternative cues to morphological structure. we find that morfessor is worse at segmenting real words, suggesting that successful learning by nms requires sensitivity to more cues than just statistical recurrence. ## analysis 1: sensitivity to templates our first analysis examines morfessor and nms segmentations of māori words formed through different morphological processes. for each learner, we identify the sensitivity to general templates and the importance of morphological concatenativity by comparing segmentation performance across words formed by reduplication and words formed by affix-ation or compounding (section 3.2), as well across cases of affixation that follow salient allomorphic templates to different extents (section 3.3). this analysis reveals how unsupervised learning of morphological segmentation is sensitive to linguistic structure, and the extent to which the underlying assumptions of morfessor make it a plausible model of naive statistical learning of morphological segmentation in humans. ## analysis 2: other cues analysis 1 showed that nms are sensitive to templates, both at the word level (reduplication) and at the morph level (minimality constraints; allomorphs that follow a phonological template or feature syllables with canonical cv shape). morfessor shows no such sensitivity, as its underlying genera-tive model does not incorporate templates, and thus underperforms when segmenting words that invoke templates in some way. however, templates appear not to be the only reason that morfessor underperforms. in section 3.2.2, we observed that morfessor's performance on compounds was lower than might be expected, given that they follow its underlying assumption of morphological concatenativity. based on this observation, we suggested that the morphological structure of māori may be cued by more than the statistical recurrence of forms, consistent with previous results showing that ms segmentations are sensitive to aspects such as the presence of long vowels @xcite . here, we explore this suggestion further by comparing morfessor's performance on real māori words, which may contain such additional cues to morphological structure, to its performance on artificially constructed pseudo-māori words, which are governed by the same patterns of statistical recurrence of morphs but lack any additional cues to morphological structure. this analysis reveals the extent to which such additional cues exist in real māori and the extent to which they present issues for morfessor. in doing so, it generalizes conclusions from section 3 that the suitability of morfessor to a particular language -and, by extension, the extent to which statistical learning by non-speakers of that language may be based purely on tracking of statistical recurrence -is dependent upon the morphological structure of the language. ## discussion & conclusions we have examined morphological segmentations of māori by morfessor and non-māori-speaking new zealanders (nms), across words formed through a variety of morphological processes, to assess the ways in which they are affected by structural factors and the extent to which they have such effects in common. our results show that both learners are affected by linguistic structure. in some circumstances, they are affected similarly; for example, both are successful in segmenting words formed by concatenative morphological processes (analysis 1a), especially when highly frequent morphs are involved (analysis 1b). in other circumstances, they are affected in opposite ways; for example, morfessor suffers decreased segmentation performance on words that are formed via templatic processes (analysis 1a) or that cue morphological structure by means other that statistical recurrence of forms (analysis 2), whereas nms see increased performance in such cases. these similarities and differences are important when considering the nature of human statistical learning of morphological segmentation. since morfessor's learning is underpinned by a set of well defined assumptions and principles (section 2.2), the extent to which its performance aligns with that of nms may be taken to reflect the extent to which nms' learning is underpinned by those same assumptions and principles. the similarities affirm that nms undergo statistical learning, identifying and extracting statistically recurrent forms to build a memory-store of morphs. at the same time, the differences show that learning for nms does not just involve tracking statistical recurrence, but also involves inducing abstract templates about the formation of words and the shapes of (allo)morphs, as well as developing sensitivities to prominent features such as the presence of long vowels @xcite . these findings echo results showing that adults and infants attend to phonological templates when learning to segment artificial languages through incidental exposure @xcite . on a practical front, the similarities and differences in the segmentation performances of morfessor and nms suggest that human statistical learning of morphological structure can be appropriately modeled by unsupervised machine learning, but perhaps only to a first approximation, depending on the underlying assumptions of the model. when the morphological structures closely follow those assumed by the model, the morphs that the model learns can reflect the cognitive units that humans seem to operate over (e.g., @xcite . but when morphological structures vary too widely from those assumed by the model -either within a language, based on words formed by different processes, or across languages -there is the potential for the model to miss factors that are salient to humans but that it is not equipped to handle. this is especially important as different models have different underlying assumptions, which can respond differently to variation in morphological structure @xcite . the differences in the segmentation performances of morfessor and nms across words of different morphological structures not only inform the use of unsupervised morphological segmentation models as cognitive models, but also highlight potential factors that could be incorporated into segmentation models to improve their results. for example, inspired by the observation that reduplication templates are salient to humans but not to morfessor, @xcite show that adding reduplication templates to morfessor improves its ability to find reduplication in māori words. similarly, future research that dissects nms' underlying learning mechanisms could reveal additional generalizable factors that help improve the crosslinguistic applicability of unsupervised models.