Modelling Verbal Morphology in N en (2020)

Abstract: Nen verbal morphology is particularly complex; a transitive verb can take up to 1,740 unique forms. The combined effect of having a large combinatoric space and a low-resource setting amplifies the need for NLP tools. Nen morphology utilises distributed exponence - a non-trivial means of mapping form to meaning. In this paper, we attempt to model Nen verbal morphology using state-of-the-art machine learning models for morphological reinflection. We explore and categorise the types of errors these systems generate. Our results show sensitivity to training data composition; different distributions of verb type yield different accuracies (patterning with E-complexity). We also demonstrate the types of patterns that can be inferred from the training data, through the case study of sycretism.

## introduction a long-standing research direction in nlp targets the development of robust language technology applicable across the wide variety of the world's languages. unfortunately, the vast majority of machine learning models are being developed for a small fraction of nearly 7,000 languages in the world, such as english, german, french, or chinese. with introduction of highly multilingual corpora such as universaldependencies @xcite and unimorph @xcite the situation started to change. for instance, sigmorphon organized a number of shared tasks on morphological reinflection starting from 10 languages in 2016 @xcite and up to 90 languages in 2020 @xcite . in 2020, languages were sampled from various typologically diverse families: indo-european, oto-manguean, tungusic, turkic, niger-congo, bantu, and others. still, just one language, namely, murrinh-patha, an australian aboriginal language @xcite , represented the whole linguistic variety of the oceania region. in this paper, we aim at filling the gap by exploring nen, a papuan language spoken by approximately 400 people in papua new guinea. nen is known for its rich verbal morphology, with a transitive verb inflecting for up to 1, 740 feature combinations. distributed exponence, the phenomenon which gives rise to this large paradigm size, provides insight into modelling complex mappings between surface forms and feature bundles. we conduct a series of experiments on morphological reinflection task recently introduced under the umbrella of sigmorphon @xcite . we train several state-of-the-art machine learning models for verbal inflection in nen and provide an extensive error analysis. we investigate the relationship between the distribution of verb type (inflection classes) in the data and performance. finally, we show that the system learns properties of the data that are not explicitly given, but may be inferred. the rest of the paper is organized as follows: in section 2, we give a brief overview of related work. section 3 provides an overview of nen verbal morphology, section 4, details our methodology, and section 5 presents our results. finally, section 6 concludes the paper. in our error analysis, we follow the error taxonomy proposed by @xcite upon a detailed analysis of typical errors produced by morphologically reinflection systems. a similar study was conducted for tibetan @xcite . ## the nen language nen is a papuan language of the morehead-maro (or yam) family, located in the southern part of new guinea @xcite . it is spoken in the village of bimadbn in the western province of papua new guinea, by approximately 400 people, for which it is a primary language @xcite . most inhabitants are multilingual, typically speaking several of the neighbouring languages. the subject of this paper -verbs -are the most complicated word-class in nen @xcite . they are demarcated into three separate categories: prefixing, middle, and ambifixing verbs. the latter two are mostly regular in terms of morphophonological rules. in the remainder of this section, we elaborate on these characteristics, to give the reader enough background to follow the discussion in subsequent sections. ## results ## conclusion diversity representation of languages in nlp is vital to test the generalisations of models. we present the first-ever neural network-based analysis of nen, the first representation of the yam language family and to the best of our knowledge, of a papuan language. nen provides an interesting case study as it exhibits non-monotonic morphological mapping: distributed exponence. we compare state-of-the-art models for morphological reinflection across various training sizes and two sampling methods: random and zipfian. the results show no significant difference between sampling methods, and minor differences may be attributed to training set composition differences. in the zipfian case, the prefixing verb types are over-represented as they are more frequent in natural speech. we provide extensive analysis of types of errors generated by each system and show that the most common error type is allomorphy errors; a misapplication of morphophonological rules, or feature category mappings. we introduce a new subcategory of error type: free variation, which is a consequence of the natural speech origins of the corpus. we further explore composition effects by generating training sets with incremental distributions for the three verb classes noted. as expected, we found that the models trained with one class had higher prediction accuracy for that class. across homogeneous compositions, the prefixing verb class performed the best. this is likely due to a smaller e-complexity -or more simply -a smaller combination of feature tags for which the system must learn mappings. finally, we explore the likelihood of learning syncretic behaviour and using this as a predictor for an unseen feature bundle -the second singular past perfective. overwhelmingly, the system incorrectly predicts syncretism with over 80% for the a&g system and 90% for the m&c system. these results highlight that these systems can infer patterns from the data sets provided. although in our case the prediction of syncretism mirrors that of a human learner, there may be underlying, unwanted properties learnt from the data given, which calls for careful preparation of data and observation of output.