Restoring Hebrew Diacritics Without a Dictionary

Elazar Gershuni, Yuval Pinter


Abstract
We demonstrate that it is feasible to accurately diacritize Hebrew script without any human-curated resources other than plain diacritized text.We present Nakdimon, a two-layer character-level LSTM, that performs on par with much more complicated curation-dependent systems, across a diverse array of modern Hebrew sources.The model is accompanied by a training set and a test set, collected from diverse sources.
Anthology ID:
2022.findings-naacl.75
Volume:
Findings of the Association for Computational Linguistics: NAACL 2022
Month:
July
Year:
2022
Address:
Seattle, United States
Venues:
Findings | NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1010–1018
Language:
URL:
https://aclanthology.org/2022.findings-naacl.75
DOI:
10.18653/v1/2022.findings-naacl.75
Bibkey:
Cite (ACL):
Elazar Gershuni and Yuval Pinter. 2022. Restoring Hebrew Diacritics Without a Dictionary. In Findings of the Association for Computational Linguistics: NAACL 2022, pages 1010–1018, Seattle, United States. Association for Computational Linguistics.
Cite (Informal):
Restoring Hebrew Diacritics Without a Dictionary (Gershuni & Pinter, Findings 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.findings-naacl.75.pdf
Software:
 2022.findings-naacl.75.software.zip
Code
 elazarg/nakdimon
Data
Nakdimon-testNakdimon-train