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"title": "Towards a First Automatic Unsupervised Morphological Segmentation for Inuinnaqtun",
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"abstract": "Low-resource polysynthetic languages pose many challenges in NLP tasks, such as morphological analysis and Machine Translation, due to available resources and tools, and the morphologically complex languages. This research focuses on the morphological segmentation while adapting an unsupervised approach based on Adaptor Grammars in low-resource setting. Experiments and evaluations on Inuinnaqtun, one of Inuit language family in Northern Canada, considered a language that will be extinct in less than two generations, have shown promising results.",
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"text": "Low-resource polysynthetic languages pose many challenges in NLP tasks, such as morphological analysis and Machine Translation, due to available resources and tools, and the morphologically complex languages. This research focuses on the morphological segmentation while adapting an unsupervised approach based on Adaptor Grammars in low-resource setting. Experiments and evaluations on Inuinnaqtun, one of Inuit language family in Northern Canada, considered a language that will be extinct in less than two generations, have shown promising results.",
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"text": "NLP has significant achievements when dealing with different types of languages, such as isolating, inflectional or agglutinative language families. However, Indigenous polysynthetic languages still pose several challenges within NLP tasks and applications, such as morphological analysis or machine translation, due to their complex linguistic particularities and due to the scarcity of linguistic resources and reliable tools (Littell et al., 2018; Micher, 2019; Le Ngoc and Sadat, 2020) .",
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"text": "Herein, we propose an unsupervised morphological segmentation approach, which is primarily based on the grammar containing production rules, non-terminal and terminal symbols, and a lexicon using Adaptor Grammars (Johnson, 2008) . Our current research investigates Inuinnaqtun -a polysynthetic language spoken in Northern Canada, in the Inuit language family. Inuinnaqtun is considered as a language that will be extinct in less than two generations 1 .",
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"text": "Regarding the Eskimo-Aleut language family including the Inuit, unlike words in English, the word structure of Eskimo are very variable in their form (Lowe, 1985; Kudlak and Compton, 2018) . Words may be very short, built up of three formative elements such as word base, lexical suffixes, and grammatical ending suffixes, or very long, with up to ten or even fifteen formative morphemes depending on the dialect.",
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"text": "\u2022 Eskimo word structure = Word base + Lexical suffixes + Grammatical ending suffixes A single word can be used to express a whole sentence in English. The following example, extracted from (Lowe, 1985) , illustrates the polysynthesis effect of umingmakhiuriaqtuqatigitqilimaiqtara, an Inuinnaqtun sentence-word, split up into several morphemes: umingmak-hiu-riaqtu-qati-gi-tqi-limaiq-ta-ra muskox -hunt -go in order to -partner -have as -again -will no more -I-him (Meaning: I will no more again have him as a partner to go hunting muskox.)",
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"text": "We observe there is a general tendency to increase the lexical constituents with a word-base by adding more formative elements. A single word can express the meaning of a whole sentence. Moreover, morphology is highly developed and has extensive use of lexical and grammatical ending suffixes. All these linguistic aspects make the morphological segmentation task for polysynthetic languages more challenging. On the other hand, the benefit of this work helps to identify more unknown word bases by deducting from the known affixes, which in turn helps to enrich the Inuinnaqtun lexicon. The global contribution consists of helping to revitalize and preserve low-resource Indigenous languages and the transmission of the related ancestral knowledge and culture.",
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"text": "The structure of this paper is described as follows: Section 2 presents relevant works. Section 3 describes our proposed approach. Then, Section 4 presents experiments and evaluations. Finally, Section 5 gives some conclusions and perspectives for future research.",
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"text": "2 Related work Creutz and Lagus (2007) proposed the Morfessor, for the unsupervised discovery of morphemes. This work was based on Hidden Markov Model for learning the unsupervised morphological segmentation, and by using the hierarchical structure of the morphemes. This framework became a benchmark in unsupervised morphological analysis, such as Morfessor 2.0 (Virpioja et al., 2013) . Johnson (2008) proposed Adaptor Grammars approach that was successful for the unsupervised morphological segmentation. This approach used non-parametric Bayesian models generalizing probabilistic context-free grammar (PCFG). In this approach, a PCFG is considered as a morphological grammar of word structures. Then the AG models can be able to induce the segmentation at the morpheme level.",
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"text": "This approach has been extended in several studies (Botha and Blunsom, 2013; Sirts and Goldwater, 2013; Eskander et al., 2018) for learning non-concatenative morphology, or for unsupervised morphological segmentation of unseen languages. Recently, Godard et al. (2018) applied AG approach for the linguists with word segmentation experiments for very low-resource African languages. Eskander et al. (2019) has applied the AG approach in an unsupervised morphological segmentation of the low-resource polysynthetic languages such as Mexicanero, Nahuatl, Yorem Nokki and Wixarika. Their evaluations have shown a significant improvement up to 87.90% in terms of F1-score, compared to the supervised approaches (Kann et al., 2018) . Our work examines the efficiency of the AG-based approach on Inuinnaqtun, a polysynthetic low-resource Inuit language.",
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"text": "Inspired by the work of Eskander et al. (2019), we adapt an unsupervised morphological segmentation with the Adaptor Grammars (AG) approach for the Inuit language family, by completing an empirical study on Inuinnaqtun.",
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"text": "The main process consists of defining (1) the grammar including non-terminal, terminal symbols, a set of production rules, and (2) collecting a large amount of unsegmented word list in order to discover and to learn all possible morphological patterns.",
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"text": "In our work, we consider that word structures are specified in the grammar patterns where a word is constituted as one word base, a sequence of possible lexical suffixes and grammatical ending suffixes (see Table 1 ). In contrast, as explained in (Eskander et al., 2019) , the word structure is composed of a sequence of prefixes, a stem and a sequence of suffixes. Then, in each production rule, a and b are two parameters of Pitman-Yor process (Pitman and Yor, 1997) . Setting a = 1 and b = 1 indicate, to the running learner, the current non-terminals are not adapted and sampled by the general Pitman-Yor process. Otherwise, the current non-terminals are adapted and expanded as in a regular probabilistic context-free grammar.",
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"text": "In order to adapt the AG scholar-seeded setting with linguistic knowledge, we have collected a list of affixes from dictionaries and Websites in the appropriate language.",
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"text": "In order to train the Adaptor Grammars-based unsupervised morphological segmentation model, the two principal inputs consists of the grammar and the lexicon of the language. The lexicon consists of a unique list of unsegmented words, more than 50K words, with the sequence length between three letters and 30 letters.",
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"text": "We collected manually a small corpus from several resources such as the Website of Nunavut 2 government for Inuinnaqtun, open source dictionaries and grammar books (Lowe, 1985; Kudlak and Compton, 2018) . The experimental corpus contains 190 word bases and 571 affixes. A small golden testing set is manually crafted containing 1,055 unique segmented words.",
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"text": "We used the MorphAGram toolkit (Eskander et al., 2020) to train our unsupervised morphological segmentation model. Following (Eskander et al., 2019) , we set up the same configuration with adaptation of the best learning settings: the best standard PrefixStemSuffix+SuffixMorph grammar and the best scholar-seeded grammar, that become here an adaptation of the standard grammar Word-Base+LexicalSuffix+GrammaticalSuffix pattern for 1 1 Word ->WordBase LexicalSuffix GrammaticalSuffix WordBase ->^^\u0174 ordBase ->^^^WordBaseMorphs 1 1 WordBaseMorphs -> WordBaseMorph WordBaseMorph -> SubMorphs LexicalSuffix -> SubMorphs LexicalSuffix -> SuffixMorphs $$$ LexicalSuffix -> $$$ GrammaticalSuffix -> SuffixMorphs $$$ 1 1 SuffixMorphs -> SuffixMorph SuffixMorphs 1 1 SuffixMorphs -> SuffixMorph 1 1 SubMorphs -> SubMorph SubMorphs 1 1 SubMorphs -> SubMorph SubMorph -> Chars 1 1 Chars -> Char 1 1 Chars -> Char Chars Table 1 : Adaptation of the standard grammar WordBase+LexicalSuffix+GrammaticalSuffix pattern for Inuinnaqtun. The symbols^^^and $$$ mean the beginning and the end of the word sequence, respectively. Source: see the standard PrefixStemSuffix+SuffixMorph grammar pattern (Eskander et al., 2019) .",
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"text": "Ground Inuinnaqtun (see Table 1 ). We evaluate our different models against the baseline, based on Morfessor (Virpioja et al., 2013) .",
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"text": "All the model performances are calculated using common evaluation metrics, such as Precision (P), Recall (R) and F1 score.",
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"text": "EQUATION",
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"raw_str": "P = |{relevant tokens} \u2229 {found tokens}| {found tokens} (1) R = |{relevant tokens} \u2229 {found tokens}| {relevant tokens} (2) F1 = 2 \u00d7 P \u00d7 R P + R",
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"text": "where {found tokens} means the amount of predicted tokens; and {relevant tokens} indicates the amount of tokens which are correctly segmented. Tables 2 and 3 show some illustrations of prediction by all the models and the performance of our models versus Morfessor as baseline on the test set. The AG-standard model is better than the baseline, with a gain of +2.47%, +4.9% in terms of precision and recall, on the test set, respectively. Both baseline and AG-Standard models obtained low precision between 48.29% and 50.76%. We observed an over-segmentation in both models.Furthermore, we noticed that the scholar-seeded learning outperformed all the baseline and the standard setting, with performances of 71.06%, 82.83%, 76.49% in terms of Precision, Recall and F1 score, respectively. Our models tend to over-segment more complex morphemes due to the linguistic irregularities and the morphophonological phenomena, to detect common lexical suffixes such as at, aq, iq, na, ng or grammatical ending suffixes such as a, k, q, t, n, it, mi or uk. ",
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"text": "In this research paper, we presented how to build the unsupervised morphological segmentation with Adaptor Grammars approach for Inuinnaqtun, an Inuit language, considered as an extremely low-resource polysynthetic language, that will be extinct in less than two generations, as described and referenced above. This Adaptor Grammars-based approach showed promising results, when using a set of grammar rules, that can be collected from grammar books; and a lexicon extracted from very little data. As a perspective, we intend to develop more efficient unsupervised morphological segmentation methods and to extend our research to other Indigenous languages and dialects, especially the very endangered ones; with applications on Machine Translation and Information Retrieval.",
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"section": "Conclusion",
"sec_num": "5"
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"text": "https://www.kitikmeotheritage.ca/ language",
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"text": "https://www.gov.nu.ca/in/cgs-in",
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"text": "The authors wish to express their thanks for professor Richard Compton, Canada Research Chair in Knowledge and Transmission of the Inuit Language, and the reviewers, for their constructive feedbacks.",
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