Modelling Verbal Morphology in Nen

• URL: http://arxiv.org/abs/2011.14489v2
• Date: Sun, 6 Dec 2020 23:08:01 GMT
• Title: Modelling Verbal Morphology in Nen
• Authors: Saliha Murado\u{g}lu, Nicholas Evans, Ekaterina Vylomova
• Abstract summary: We use state-of-the-art machine learning models for morphological reinflection to model Nen verbal morphology. Our results show sensitivity to training data composition; different distributions of verb type yield different accuracies. We also demonstrate the types of patterns that can be inferred from the training data through the case study of syncretism.
• Score: 4.6877729174041605
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Nen verbal morphology is remarkably 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 syncretism.

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