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
- Editors:
- Marine Carpuat, Marie-Catherine de Marneffe, Ivan Vladimir Meza Ruiz
- Venue:
- Findings
- 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
- 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)
- PDF:
- https://preview.aclanthology.org/dois-2013-emnlp/2022.findings-naacl.75.pdf
- Code
- elazarg/nakdimon
- Data
- Nakdimon-test, Nakdimon-train