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# Restoring Hebrew Diacritics Without a Dictionary

**Authors:** Elazar Gershuni; [Yuval Pinter](https://md.mova.elamur.ai/free/en/docs/papers/authors/yuval-pinter/) · **Year:** 2022 · **Venue:** Findings of the Association for Computational Linguistics: NAACL 2022, pp. 1010–1018 **Link:** [aclanthology.org/2022.findings-naacl.75](https://aclanthology.org/2022.findings-naacl.75/) · library `findings-2022-gershuni-restoring-hebrew-diacritics-without-dictionary` · **Licence:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) (ACL Anthology)

[**1** citations](https://openalex.org/works?filter=cites:W3160881140) openalex-snapshot · 2026-10-14[**8** citations in our library](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/findings-2022-gershuni-restoring-hebrew-diacritics-without-dictionary/) of the 140673 papers whose bibliographies we read[Findings of the Association for Computational Linguistics: NAACL 2022](https://aclanthology.org/volumes/2022.findings-naacl/)[licence](https://creativecommons.org/licenses/by/4.0/)

- [Publisher's page](https://aclanthology.org/2022.findings-naacl.75/)
- [PDF](https://aclanthology.org/2022.findings-naacl.75.pdf)
- [DOI 10.18653/v1/2022.findings-naacl.75](https://doi.org/10.18653/v1/2022.findings-naacl.75)
- [OpenAlex](https://openalex.org/W3160881140)

**Authors (2)**

1. [Elazar Gershuni](https://openalex.org/A5041476153) [Semantic Scholar](https://www.semanticscholar.org/author/133892454) · h 1 · 99 citations · 7 works
2. [Yuval Pinter](https://openalex.org/A5008477284) [ORCID](https://orcid.org/0000-0003-3174-1621) · [Semantic Scholar](https://www.semanticscholar.org/author/1826312) · h 14 · 1663 citations · 67 works

Citations and author metrics from openalex-snapshot (2026-10-14); authors with the same name are not always told apart.

## 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/hbo` reports 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](https://md.mova.elamur.ai/free/en/docs/languages/hbo/01-script/). 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](https://md.mova.elamur.ai/free/en/docs/languages/he/12-mova/). The character BiLSTM is not a Mova component; the Mova version of the three-head design is rules with a measured error table.

## Cite

The paper itself, not this card. BibTeX from the library's record of the work. [DOI 10.18653/v1/2022.findings-naacl.75](https://doi.org/10.18653/v1/2022.findings-naacl.75)

```
@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}
}
```

- [← previous A Novel Challenge Set for Hebrew Morphological Disambiguation and Diacritics Restoration](https://md.mova.elamur.ai/free/en/docs/papers/findings-2020-shmidman-novel-challenge-set-hebrew-morphological/)
- [next → MRL Parsing Without Tears: The Case of Hebrew](https://md.mova.elamur.ai/free/en/docs/papers/findings-2024-shmidman-mrl-parsing-without-tears-case/)
- [↑ back to Ancient Hebrew](https://md.mova.elamur.ai/free/en/docs/papers/hbo/)

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