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Shaltiel Shmidman

13 paper cards in Mova's library · 2020–2026

Shaltiel Shmidman wrote 13 of the papers Mova reads, in 2020–2026, most of them on Part-of-speech tagging, Treebanks; often with Avi Shmidman, Moshe Koppel, Amir DN Cohen.

OpenAlex · h-index 3 · 30 citations · 21 works — openalex, 2026-10-18

The profile is the library's match of the name; two people with one name are not always told apart.

Elsewhere: Google Scholar · Semantic Scholar · dblp · ACL Anthology · ORCID

Topics: Part-of-speech tagging 2 · Treebanks 1

Ideas Mova took (1)

From the ideas ledger: an idea tried, kept or queued, or measured with no gain, and what it gave.

Papers

  1. Dicta-LM 3.0: Advancing The Frontier of Hebrew Sovereign LLMs (2026)
    Dicta-LM 3.0 is an open-weight family of Hebrew-and-English LLMs at 24B, 12B and 1.7B parameters. Each was made by continued pre-training of an existing base model (Mistral-Small-3.1, Nemotron Nano V2, Qwen3-1.7B) on about 100B Hebrew tokens mixed with 30B English tokens, then post-trained with SFT, DPO and GRPO for…
  2. HEBATRON: A Hebrew-Specialized Open-Weight Mixture-of-Experts Language Model (2026)
    The authors adapt NVIDIA's Nemotron-3 Nano sparse mixture-of-experts model (30B parameters, about 3B active per token) to Hebrew.
  3. NeoDictaBERT: Pushing the Frontier of BERT models for Hebrew (2025)
    The authors move the NeoBERT encoder design (pre-norm RMSNorm, RoPE, SwiGLU, 28 layers at width 768, 4,096-token context) to Hebrew.
  4. Restoring Missing Spaces in Scraped Hebrew Social Media (2025)
    Scraped web text often runs two words together, and in unvocalised Hebrew one conflated string can usually be split in several valid ways, so a dictionary lookup does not settle it.
  5. MRL Parsing Without Tears: The Case of Hebrew (2024)
    Parsers for morphologically rich languages either run a pipeline (segment, tag, parse), which passes errors forward, or a joint lattice over all segmentations, which is slow and needs a lexicon.
  6. Adapting LLMs to Hebrew: Unveiling DictaLM 2.0 with Enhanced Vocabulary and Instruction Capabilities (2024)
    The paper adapts Mistral-7B to Hebrew by continued pre-training on about 200B tokens of half Hebrew and half English.
  7. OtoBERT: Identifying Suffixed Verbal Forms in Modern Hebrew Literature (2024)
    Modern Hebrew literature sometimes fuses a verb with its direct-object pronoun into one word. The fused form is often identical to a common verb with another meaning, so readers are misled until the sentence ends. Annotators found 222 suffixed verbs in 135K words of literature (17.76 per 10K) against 7 in 185K words…
  8. DictaBERT: A State-of-the-Art BERT Suite for Modern Hebrew (2023)
    The paper presents DictaBERT, a BERT-architecture model for Modern Hebrew, pre-trained with masked language modelling only (next-sentence prediction removed) on about 3.8B tokens: 70% cleaned HeDC4 web text and 30% news, blogs, subtitles and novels.
  9. Introducing DictaLM -- A Large Generative Language Model for Modern Hebrew (2023)
    The authors release DictaLM, a 7B-parameter generative model for Modern Hebrew, trained on about 7.5 billion tokens.
  10. Introducing BEREL: BERT Embeddings for Rabbinic-Encoded Language (2022)
    A short paper releasing a BERT-base model trained on about 220 million words of Rabbinic Hebrew (the Sefaria and Dicta libraries).
  11. Large Pre-Trained Models with Extra-Large Vocabularies: A Contrastive Analysis of Hebrew BERT Models and a New One to Outperform Them All (2022)
    The authors train AlephBERTGimmel, a Modern Hebrew BERT with a 128K word-piece vocabulary (earlier models had 2.4K–52K), on the same ~2 billion words as AlephBERT.
  12. Nakdan: Professional Hebrew Diacritizer (2020)
    Nakdan adds vowel points to undiacritized Hebrew, for modern, Rabbinic and poetic texts, through a web interface that lets an editor choose among the alternatives.
  13. A Novel Challenge Set for Hebrew Morphological Disambiguation and Diacritics Restoration (2020)
    Hebrew homographs are often unbalanced: one analysis is 30 to 129 times more frequent than the other. A tagger can then score well on the majority reading and fail on the minority one, which is the reading that matters in rare cases. The authors build a challenge set of 21 homographs (12 unbalanced, 9 balanced) with…

Co-authors (23)

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