Surface-Level Morphological Segmentation of Low-resource Inuktitut Using Pre-trained Large Language Models (2025)

Abstract: Segmenting languages based on morpheme boundaries instead of relying on language independent segmenting algorithms like Byte-Pair Encoding (BPE) has shown to benefit downstream Natural Language Processing (NLP) task performance. This can however be tricky for polysynthetic languages like Inuktitut due to a high morpheme-to-word ratio and the lack of appropriately sized annotated datasets. Through our work, we display the potential of using pre-trained Large Language Models (LLMs) for surface-level morphological segmentation of Inuktitut by treating it as a binary classification task. We fine-tune on tasks derived from automatically annotated Inuktitut words written in Inuktitut syllabics. Our approach shows good potential when compared to previous neural approaches. We share our best model to encourage further studies on down stream NLP tasks for Inuktitut written in syllabics.

## introduction the inuktitut language, indigenous to the northernmost regions of canada and spoken by roughly 40k speakers, is particularly difficult to adapt nlp tools for. not only is the lack of appropriately sized annotated datasets a big hurdle, but so is the polysynthetic nature of the language itself. this linguistic attribute results in a very high average morpheme-to-word ratio, by some estimates as high as 4.39 @xcite , where often times one or two words in inuktitut can express what would take a full sentence to express in english @xcite (see figure 1 ). naturally, this leads to numerous ways of forming unique and rare words, each one conveying rich linguistic information. figure 1 : an example of an inuktitut word written in inuktitut syllabics, romanized as "parimunngauniralauqsimanngittunga", translating to a full sentence in english. to combat similar issues with rare or unique words in other languages, a common practice is to pre-process textual data by deploying algorithms such as bpe @xcite or sentence-piece @xcite ) that are efficient at breaking up words into more digestible sub-strings. however, these algorithms are language independent and split words based on the frequency of commonly occurring sub-string character clusters and not on the basis of actual linguistic information. instead, we turn our attention to surface-level morphological segmentation, as explicit morphological information has shown to be valuable for various down steam nlp tasks @xcite @xcite , especially for low-resource languages @xcite . despite the existence of an invaluable rule-based tool @xcite capable of segmenting inuktitut based on linguistic information, it is not reliable as it fails to return segmentations for many words. in this study, we explore a different approach to segmenting inuktitut compared to previous efforts by leveraging off a pre-trained multilingual llm and by turning surface-level morphological segmentation into a binary classification task through the use of llmsegm @xcite . we annotate additional training data using the existing rule-based segmentation tool and evaluate our fine-tuned models on a variation of human annotated and automatically annotated test sets. contrarily to the majority of previous studies, which employ the romanized version of the language, our setup focuses on segmenting inuktitut written in inuktitut syllabics. by sharing our best performing model, we hope to inspire others to also conduct their research on inuktitut written in syllabics without romanizing the language first. our main contributions are: ## background and related work there are plenty of methods dealing with morphological segmentation. here we mention a few related to our work. @xcite introduced an unsupervised probabilistic morpheme identifying method that has seen widespread use, with many related projects following their lead @xcite . more recently, @xcite introduced mor-phagram, another unsupervised approach based on adaptor grammars @xcite . semisupervised methods incorporating conditional random fields have also been proposed @xcite , as well as fully supervised ones @xcite . additionally, there have been numerous neural approaches @xcite @xcite using various model architectures. recently, @xcite leveraged off pre-trained llms to segment words by turning morphological segmentation into a binary classification task. they displayed the effectiveness of their approach for a number of languages in a low-resource setting. additionally, surface-level segmentation as a community task has also been highlighted during the 2005 to 2010 morpho challenges @xcite and for a few low-resource languages in the shared task lowresourceeval-2019 @xcite . ## results & discussion we present our results in ## conclusion we contribute to ongoing research focusing on the polysynthetic language inuktitut by fine-tuning and sharing a glot500-m llm for binary classification of morpheme boundaries. our best model shows promising results when comparing to previous efforts, despite struggling to segment words with fewer true segmentation boundaries. we also show the potential of deploying existing pre-trained llms using llmsegm even for under-resources polysynthetics languages without the need to train anything from scratch. additionally, we further encourage future studies on down stream nlp tasks for inuktitut written in syllabics. in future efforts, we intend to improve the performance of our model, as well as investigate its potential as a pre-processing tool for down stream nlp tasks such as machine translation.