One core challenge facing morphological inflection systems is capturing language-specific morphophonological changes. This is particularly true of languages like Arabic which are morphologically complex. In this paper, we learn explicit morphophonological rules from morphologically annotated Egyptian Arabic and corresponding surface forms. These rules are human-interpretable, capture known morphophonological phenomena in the language, and are generalizable to unseen forms.
CITATION STYLE
Khalifa, S., Kodner, J., & Rambow, O. (2022). Towards Learning Arabic Morphophonology. In WANLP 2022 - 7th Arabic Natural Language Processing - Proceedings of the Workshop (pp. 295–301). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/2022.wanlp-1.27
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