SIGMORPHON 2022 Task 0 Submission Description: Modelling Morphological Inflection with Data-Driven and Rule-Based Approaches

Tatiana Merzhevich, Nkonye Gbadegoye, Leander Girrbach, Jingwen Li, Ryan Soh-Eun Shim


Abstract
This paper describes our participation in the 2022 SIGMORPHON-UniMorph Shared Task on Typologically Diverse and AcquisitionInspired Morphological Inflection Generation. We present two approaches: one being a modification of the neural baseline encoderdecoder model, the other being hand-coded morphological analyzers using finite-state tools (FST) and outside linguistic knowledge. While our proposed modification of the baseline encoder-decoder model underperforms the baseline for almost all languages, the FST methods outperform other systems in the respective languages by a large margin. This confirms that purely data-driven approaches have not yet reached the maturity to replace trained linguists for documentation and analysis especially considering low-resource and endangered languages.
Anthology ID:
2022.sigmorphon-1.20
Volume:
Proceedings of the 19th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology
Month:
July
Year:
2022
Address:
Seattle, Washington
Venue:
SIGMORPHON
SIG:
SIGMORPHON
Publisher:
Association for Computational Linguistics
Note:
Pages:
204–211
Language:
URL:
https://aclanthology.org/2022.sigmorphon-1.20
DOI:
10.18653/v1/2022.sigmorphon-1.20
Bibkey:
Cite (ACL):
Tatiana Merzhevich, Nkonye Gbadegoye, Leander Girrbach, Jingwen Li, and Ryan Soh-Eun Shim. 2022. SIGMORPHON 2022 Task 0 Submission Description: Modelling Morphological Inflection with Data-Driven and Rule-Based Approaches. In Proceedings of the 19th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology, pages 204–211, Seattle, Washington. Association for Computational Linguistics.
Cite (Informal):
SIGMORPHON 2022 Task 0 Submission Description: Modelling Morphological Inflection with Data-Driven and Rule-Based Approaches (Merzhevich et al., SIGMORPHON 2022)
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PDF:
https://aclanthology.org/2022.sigmorphon-1.20.pdf