@inproceedings{scherbakov-2020-unimelb,
    title = "The {U}ni{M}elb Submission to the {SIGMORPHON} 2020 Shared Task 0: Typologically Diverse Morphological Inflection",
    author = "Scherbakov, Andreas",
    editor = "Nicolai, Garrett  and
      Gorman, Kyle  and
      Cotterell, Ryan",
    booktitle = "Proceedings of the 17th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2020.sigmorphon-1.20/",
    doi = "10.18653/v1/2020.sigmorphon-1.20",
    pages = "177--183",
    abstract = "The paper describes the University of Melbourne`s submission to the SIGMORPHON 2020 Shared Task 0: Typologically Diverse Morphological Inflection. Our team submitted three systems in total, two neural and one non-neural. Our analysis of systems' performance shows positive effects of newly introduced data hallucination technique that we employed in one of neural systems, especially in low-resource scenarios. A non-neural system based on observed inflection patterns shows optimistic results even in its simple implementation ({\ensuremath{>}}75{\%} accuracy for 50{\%} of languages). With possible improvement within the same modeling principle, accuracy might grow to values above 90{\%}."
}