Towards a First Automatic Unsupervised Morphological Segmentation for Inuinnaqtun
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Towards a First Automatic Unsupervised Morphological Segmentation for Inuinnaqtun Tan Le Ngoc and Fatiha Sadat Université du Québec à Montréal / Montreal, Quebec, Canada 201, avenue du Président-Kennedy, H2X 3Y7 Montréal [email protected], [email protected] Abstract form (Lowe, 1985; Kudlak and Compton, 2018). Words may be very short, built up of three forma- Low-resource polysynthetic languages pose tive elements such as word base, lexical suffixes, many challenges in NLP tasks, such as mor- phological analysis and Machine Translation, and grammatical ending suffixes, or very long, with due to available resources and tools, and the up to ten or even fifteen formative morphemes de- morphologically complex languages. This re- pending on the dialect. search focuses on the morphological segmenta- tion while adapting an unsupervised approach • Eskimo word structure = Word base + Lexi- based on Adaptor Grammars in low-resource cal suffixes + Grammatical ending suffixes setting. Experiments and evaluations on Inuin- naqtun, one of Inuit language family in North- ern Canada, considered a language that will A single word can be used to express a whole sen- be extinct in less than two generations, have tence in English. The following example, extracted shown promising results. from (Lowe, 1985), illustrates the polysynthesis ef- fect of umingmakhiuriaqtuqatigitqilimaiqtara, an 1 Introduction Inuinnaqtun sentence-word, split up into several NLP has significant achievements when dealing morphemes: with different types of languages, such as isolat- umingmak-hiu-riaqtu-qati-gi-tqi-limaiq-ta-ra ing, inflectional or agglutinative language families. muskox - hunt - go in order to - partner - have However, Indigenous polysynthetic languages still as - again - will no more - I-him pose several challenges within NLP tasks and ap- (Meaning: I will no more again have him as a plications, such as morphological analysis or ma- partner to go hunting muskox.) chine translation, due to their complex linguistic We observe there is a general tendency to in- particularities and due to the scarcity of linguistic crease the lexical constituents with a word-base resources and reliable tools (Littell et al., 2018; by adding more formative elements. A single Mager et al., 2018; Micher, 2019; Le Ngoc and word can express the meaning of a whole sentence. Sadat, 2020). Moreover, morphology is highly developed and Herein, we propose an unsupervised morpho- has extensive use of lexical and grammatical end- logical segmentation approach, which is primar- ing suffixes. All these linguistic aspects make the ily based on the grammar containing production morphological segmentation task for polysynthetic rules, non-terminal and terminal symbols, and a languages more challenging. On the other hand, lexicon using Adaptor Grammars (Johnson, 2008). the benefit of this work helps to identify more un- Our current research investigates Inuinnaqtun - a known word bases by deducting from the known polysynthetic language spoken in Northern Canada, affixes, which in turn helps to enrich the Inuin- in the Inuit language family. Inuinnaqtun is consid- naqtun lexicon. The global contribution consists ered as a language that will be extinct in less than of helping to revitalize and preserve low-resource two generations1. Indigenous languages and the transmission of the Regarding the Eskimo-Aleut language family related ancestral knowledge and culture. including the Inuit, unlike words in English, the The structure of this paper is described as fol- word structure of Eskimo are very variable in their lows: Section2 presents relevant works. Section 1https://www.kitikmeotheritage.ca/ 3 describes our proposed approach. Then, Section language 4 presents experiments and evaluations. Finally, 159 Proceedings of the First Workshop on Natural Language Processing for Indigenous Languages of the Americas, pages 159–162 June 11, 2021. ©2021 Association for Computational Linguistics Section5 gives some conclusions and perspectives patterns. for future research. In our work, we consider that word structures are specified in the grammar patterns where a word is 2 Related work constituted as one word base, a sequence of possi- Creutz and Lagus(2007) proposed the Morfessor, ble lexical suffixes and grammatical ending suffixes for the unsupervised discovery of morphemes. This (see Table1). In contrast, as explained in (Eskan- work was based on Hidden Markov Model for learn- der et al., 2019), the word structure is composed of ing the unsupervised morphological segmentation, a sequence of prefixes, a stem and a sequence of and by using the hierarchical structure of the mor- suffixes. Then, in each production rule, a and b are phemes. This framework became a benchmark in two parameters of Pitman-Yor process (Pitman and unsupervised morphological analysis, such as Mor- Yor, 1997). Setting a = 1 and b = 1 indicate, to fessor 2.0 (Virpioja et al., 2013). the running learner, the current non-terminals are Johnson(2008) proposed Adaptor Grammars ap- not adapted and sampled by the general Pitman-Yor proach that was successful for the unsupervised process. Otherwise, the current non-terminals are morphological segmentation. This approach used adapted and expanded as in a regular probabilistic non-parametric Bayesian models generalizing prob- context-free grammar. abilistic context-free grammar (PCFG). In this ap- In order to adapt the AG scholar-seeded setting proach, a PCFG is considered as a morphological with linguistic knowledge, we have collected a list grammar of word structures. Then the AG mod- of affixes from dictionaries and Websites in the els can be able to induce the segmentation at the appropriate language. morpheme level. 4 Experiments This approach has been extended in several stud- ies (Botha and Blunsom, 2013; Sirts and Gold- 4.1 Data Preparation water, 2013; Eskander et al., 2018) for learning In order to train the Adaptor Grammars-based un- non-concatenative morphology, or for unsuper- supervised morphological segmentation model, the vised morphological segmentation of unseen lan- two principal inputs consists of the grammar and guages. Recently, Godard et al.(2018) applied the lexicon of the language. The lexicon consists AG approach for the linguists with word segmen- of a unique list of unsegmented words, more than tation experiments for very low-resource African 50K words, with the sequence length between languages. Eskander et al.(2019) has applied the three letters and 30 letters. AG approach in an unsupervised morphological segmentation of the low-resource polysynthetic lan- We collected manually a small corpus from sev- 2 guages such as Mexicanero, Nahuatl, Yorem Nokki eral resources such as the Website of Nunavut and Wixarika. Their evaluations have shown a sig- government for Inuinnaqtun, open source dictio- nificant improvement up to 87.90% in terms of naries and grammar books (Lowe, 1985; Kudlak F1-score, compared to the supervised approaches and Compton, 2018). The experimental corpus (Kann et al., 2018). Our work examines the effi- contains 190 word bases and 571 affixes. A small ciency of the AG-based approach on Inuinnaqtun, golden testing set is manually crafted containing a polysynthetic low-resource Inuit language. 1,055 unique segmented words. 3 Our approach 4.2 Training Settings We used the MorphAGram toolkit (Eskander et al., Inspired by the work of Eskander et al.(2019), we 2020) to train our unsupervised morphological adapt an unsupervised morphological segmentation segmentation model. Following (Eskander et al., with the Adaptor Grammars (AG) approach for the 2019), we set up the same configuration with Inuit language family, by completing an empirical adaptation of the best learning settings: the best study on Inuinnaqtun. standard PrefixStemSuffix+SuffixMorph grammar The main process consists of defining (1) the and the best scholar-seeded grammar, that become grammar including non-terminal, terminal sym- here an adaptation of the standard grammar Word- bols, a set of production rules, and (2) collecting Base+LexicalSuffix+GrammaticalSuffix pattern for a large amount of unsegmented word list in order to discover and to learn all possible morphological 2https://www.gov.nu.ca/in/cgs-in 160 1 1 Word –>WordBase LexicalSuffix GrammaticalSuffix GrammaticalSuffix –> SuffixMorphs $$$ WordBase –> ^^^ 1 1 SuffixMorphs –> SuffixMorph SuffixMorphs WordBase –> ^^^ WordBaseMorphs 1 1 SuffixMorphs –> SuffixMorph 1 1 WordBaseMorphs –> WordBaseMorph 1 1 SubMorphs –> SubMorph SubMorphs WordBaseMorph –> SubMorphs 1 1 SubMorphs –> SubMorph SubMorph –> Chars LexicalSuffix –> SubMorphs 1 1 Chars –> Char LexicalSuffix –> SuffixMorphs $$$ 1 1 Chars –> Char Chars LexicalSuffix –> $$$ Table 1: Adaptation of the standard grammar WordBase+LexicalSuffix+GrammaticalSuffix pattern for Inuinnaq- tun. The symbols ^^^ and $$$ mean the beginning and the end of the word sequence, respectively. Source: see the standard PrefixStemSuffix+SuffixMorph grammar pattern (Eskander et al., 2019). Word Ground Truth Morfessor AG-Standard AG-Scholar aullarnatin aullar na tin aulla rn at in a ulla rna tin aullar nati n havangnatik havang na tik hav ang na tik hav a ngna tik havang na tik iaqluktinnagu iqaluk tinna gu iqalu k ti nna gu iqa luk tinna gu iaqluk tinna gu nirihuiqtunga niri huiq tunga niri huiq tu ng a niri huiq tu ng a niri huiq tunga niritinnagit niri tinna git niri ti nna gi t niri tinna git niri tinna git umiarmi umiar mi umi a rmi umi armi umia r mi umiaq umiaq umi aq u mi aq umiaq tikinnanuk tikin na nuk tikinnanuk