Restoring Hebrew Diacritics Without a Dictionary
What it is about
Most Modern Hebrew is written unpointed, and dotting (adding niqqud) needs expertise or a paid service; the state of the art, Dicta's Nakdan, uses morphological analysis, dictionaries and several networks. The authors ask how far one simple model can go with plain pointed text. NAKDIMON is a two-layer character BiLSTM with three linear heads: shin/sin dot, dagesh/mappiq, and the other vowel marks, decoded greedily without any lexicon. Training uses pre-modern pointed texts (about 1.8M tokens), an automatically dotted set of stories, and about 327K tokens of modern text dotted with Dicta and hand-corrected. A new modern test set of 20K tokens from eleven sources is released. NAKDIMON is close to Morfix on character accuracy but below it on word accuracy; its errors are often unreadable vowel sequences that a reader notices, and it does well on foreign names.
Ideas
- I1. Score dotting in three separate heads (sin/shin, dagesh, vowels), as the paper does, so a rule dotter in
tools/hboreports which category fails. - I2. Report vocalisation accuracy (is the pronunciation right, even if the marks differ) next to word accuracy; the gap shows which errors matter to a reader.
- I3. Keep a held-out test drawn from several sources, to check that a dotter built on one genre does not overfit it; the paper's source table is the layout to copy.
For Mova
I1 and I2 apply to the unpointed mode of tools/hbo, to any restoration step, and to 1. Script and pointing. I3 is a test-set design for tools/hbo and, for Modern Hebrew, for 12. Mova and Modern Hebrew: where it stands, what it would need. The character BiLSTM is not a Mova component; the Mova version of the three-head design is rules with a measured error table.
Cite
@inproceedings{gershuni2022restoring,
title = {{Restoring Hebrew Diacritics Without a Dictionary}},
author = {Gershuni, Elazar and Pinter, Yuval},
booktitle = {Findings of the Association for Computational Linguistics: NAACL 2022},
year = {2022},
doi = {10.18653/v1/2022.findings-naacl.75},
url = {https://doi.org/10.18653/v1/2022.findings-naacl.75}
}References
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