Deep Learning-Based Morphological Segmentation for Indigenous Languages: A Study Case on Innu-Aimun (2022)

Abstract: Recent advances in the field of deep learning have led to a growing interest in the development of NLP approaches for low-resource and endangered languages. Nevertheless, relatively little research, related to NLP, has been conducted on indigenous languages. These languages are considered to be filled with complexities and challenges that make their study incredibly difficult in the NLP and AI fields. This paper focuses on the morphological segmentation of indigenous languages, an extremely challenging task because of polysynthesis, dialectal variations with rich morpho-phonemics, misspellings and resource-limited scenario issues. The proposed approach, towards a morphological segmentation of Innu-Aimun, an extremely low-resource indigenous language of Canada, is based on deep learning. Experiments and evaluations have shown promising results, compared to state-of-the-art rule-based and unsupervised approaches.

## introduction over the past decade, we have observed a successful growth in the deep learning-based approaches in several natural language processing (nlp) applications. this has helped to create nlp tools and applications in resource-rich languages. on the other hand, for low-resource languages, few applications of nlp have been studied for multiple reasons @xcite . in particular, for indigenous languages, nlp applications have to deal with linguistics challenges such as polysynthesis, diversity of grammatical features of morphology, dialect variation with rich morpho-phonemics, misspellings due to noisy or scarce training data and low resource scenario challenges @xcite . moreover, morphological segmentation for indigenous polysynthetic languages is especially challenging because these languages have often multiple individual morphemes by word and several meanings per morpheme. the current research focuses on the morphological segmentation task for indigenous languages, with a case study on innu-aimun, also called montagnais foot_0 . innu-aimun is an algonquian polysynthetic language spoken by over 10,000 innu in labrador and quebec in eastern canada foot_1 . we choose this indigenous language for this specific nlp task because it has not yet been investigated thus far. the main focus consists of how to develop indigenous language technology and linguistic resources, with the aim of helping the indigenous communities in the revitalization and preservation of their languages. thus, we propose in the current study, a deep learning-based morphological segmentation for innu-aimun. our contribution to the current research is twofold. firstly, it proposes a deep learning-based word segmenter for indigenous languages. secondly, it empirically compares the proposed approach, in a case study of innu-aimun, with multiple baselines such as finite-state transducer, morfessor, and adaptor grammar-based approaches. overall, this study aims to serve as a benchmark for developing nlp tools and applications, which will help revitalize and preserve indigenous languages, while taking into account indigenous cultural realities and knowledge. the paper is structured as follows: section 2 highlights morphological analyzers for indigenous languages, with a description of innu-aimun. our proposed approach is described in section 3. section 4 presents the experimental results, compared to other state-of-the-art approaches. section 5 discusses our evaluations, while providing an error analysis. finally, section 6 presents the conclusion as well as potential future work. ## error analysis due to the complex linguistic peculiarities of innu-aimun and its dialectal variations, a word can be pronounced in several ways. thus, its transcription poses more challenges in the segmentation task. besides, a word in innu-aimun is always composed of a central core (root), including a verb. with the help of an innu language teacher, we made observations and reviewed the data and predictions to determine if the segmentation results were correct and discover the errors. basically, our models tend to over-segment more complex morphemes due to the linguistic irregularities and the morphotactic phenomena, to detect common lexical suffixes such as ap, tsh or grammatical ending suffixes such as at, eu, t, n, it, mi or uk. in particular, we observed an over-segmentation in the fst and morfessor models. these models tend to segment a term into several sub-morphemes ( all models failed when dealing with out-ofvocabulary words. for example, here, the term mitshuap (meaning: house), which was not seen in the training, was segmented into multiple morphemes ( another challenge is related to the oversegmentation of all the models, down to character level, due to the length of prefixes and suffixes between one and multiple characters. for example, some models divided a term up to a character level ( ## conclusion and perspectives we presented a deep learning-based method for morphological segmentation for innu-aimun, an indigenous language of canada, which can be considered as a first research study on the subject, so far. our evaluations showed promising results. thus, the proposed deep learning-based method, incorporating pre-trained embeddings at multiple levels, helped finding morpheme boundaries related to the whole word. this study makes an important contribution by focusing on morpheme segmentation in the low-resource indigenous language. furthermore, through this research, we noted the importance of close collaboration and consultation with the innu indigenous community, to ensure that language technologies are developed with respect and in accordance with the community's revitalisation objectives.