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 (>75% accuracy for 50% of languages). With possible improvement within the same modeling principle, accuracy might grow to values above 90%.- Anthology ID:
- 2020.sigmorphon-1.20
- Volume:
- Proceedings of the 17th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology
- Month:
- July
- Year:
- 2020
- Address:
- Online
- Editors:
- Garrett Nicolai, Kyle Gorman, Ryan Cotterell
- Venue:
- SIGMORPHON
- SIG:
- SIGMORPHON
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 177–183
- Language:
- URL:
- https://aclanthology.org/2020.sigmorphon-1.20
- DOI:
- 10.18653/v1/2020.sigmorphon-1.20
- Cite (ACL):
- Andreas Scherbakov. 2020. The UniMelb Submission to the SIGMORPHON 2020 Shared Task 0: Typologically Diverse Morphological Inflection. In Proceedings of the 17th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology, pages 177–183, Online. Association for Computational Linguistics.
- Cite (Informal):
- The UniMelb Submission to the SIGMORPHON 2020 Shared Task 0: Typologically Diverse Morphological Inflection (Scherbakov, SIGMORPHON 2020)
- PDF:
- https://preview.aclanthology.org/teach-a-man-to-fish/2020.sigmorphon-1.20.pdf