@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://aclanthology.org/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 ({\textgreater}75{\%} accuracy for 50{\%} of languages). With possible improvement within the same modeling principle, accuracy might grow to values above 90{\%}.",
}
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%0 Conference Proceedings
%T The UniMelb Submission to the SIGMORPHON 2020 Shared Task 0: Typologically Diverse Morphological Inflection
%A Scherbakov, Andreas
%Y Nicolai, Garrett
%Y Gorman, Kyle
%Y Cotterell, Ryan
%S Proceedings of the 17th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F scherbakov-2020-unimelb
%X 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 (\textgreater75% accuracy for 50% of languages). With possible improvement within the same modeling principle, accuracy might grow to values above 90%.
%R 10.18653/v1/2020.sigmorphon-1.20
%U https://aclanthology.org/2020.sigmorphon-1.20
%U https://doi.org/10.18653/v1/2020.sigmorphon-1.20
%P 177-183
Markdown (Informal)
[The UniMelb Submission to the SIGMORPHON 2020 Shared Task 0: Typologically Diverse Morphological Inflection](https://aclanthology.org/2020.sigmorphon-1.20) (Scherbakov, SIGMORPHON 2020)
ACL