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# Elior Sulem

4 paper cards in Mova's library · 2018–2025

Elior Sulem wrote 4 of the papers Mova reads, in 2018–2025, most of them on [Machine translation](https://md.mova.elamur.ai/free/en/docs/papers/topics/machine-translation/); often with [Ari Rappoport](https://md.mova.elamur.ai/free/en/docs/papers/authors/ari-rappoport/), [Omri Abend](https://md.mova.elamur.ai/free/en/docs/papers/authors/omri-abend/), Anita Gelboim.

[OpenAlex](https://openalex.org/A5026220115) · [ORCID](https://orcid.org/0000-0002-9859-7313) · [**h-index 11**](https://openalex.org/authors/A5026220115) · [1958 citations](https://openalex.org/authors/A5026220115) · [40 works](https://openalex.org/works?filter=author.id:A5026220115) — 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](https://scholar.google.com/citations?view_op=search_authors&mauthors=Elior+Sulem) · [Semantic Scholar](https://www.semanticscholar.org/search?q=Elior+Sulem) · [dblp](https://dblp.org/search?q=Elior+Sulem) · [ACL Anthology](https://aclanthology.org/search/?q=Elior+Sulem) · [ORCID](https://orcid.org/orcid-search/search?searchQuery=Elior+Sulem)

Topics: [Machine translation](https://md.mova.elamur.ai/free/en/docs/papers/topics/machine-translation/) 2

## Papers

1. **[LCHAIM - Investigating Long Context Reasoning in Hebrew](https://md.mova.elamur.ai/free/en/docs/papers/findings-2025-malul-lchaim-investigating-long-context-reasoning/)** *(2025)* The authors build LCHAIM, a Hebrew natural language inference set of 8,325 premise–hypothesis pairs, by machine-translating the English ConTRoL dataset.
2. **[TafBERTa: Learning Grammatical Rules from Small-Scale Language Acquisition Data in Hebrew](https://md.mova.elamur.ai/free/en/docs/papers/babylm-2025-gelboim-tafberta-learning-grammatical-rules-small/)** *(2025)* The paper builds TafBERTa, a small RoBERTa-style masked model (3.3M parameters, 7,317 BPE items) trained from scratch on 233K words of Hebrew child-directed speech from the CHILDES Berman longitudinal corpus, rewritten in Hebrew script and merged with its %mor and %gra annotations (HTBerman: 53K sentences).
3. **[Semantic Structural Evaluation for Text Simplification](https://md.mova.elamur.ai/free/en/docs/papers/sulem-2018-semantic-structural-evaluation-text-simplification/)** *(2018)* The paper asks how to score a simplified sentence when reference simplifications are scarce and not unique.
4. **[Simple and Effective Text Simplification Using Semantic and Neural Methods](https://md.mova.elamur.ai/free/en/docs/papers/sulem-2018-simple-effective-text-simplification-semantic/)** *(2018)* The paper adds a semantic splitting step before a neural machine translation simplifier. Direct Semantic Splitting (DSS) parses each sentence with a UCCA semantic parser, which gives scenes (events with their participants), and splits the sentence at its main scenes using two simple rules, one for parallel scenes and…

## Co-authors (7)

- [Ari Rappoport](https://md.mova.elamur.ai/free/en/docs/papers/authors/ari-rappoport/) ×2
- [Omri Abend](https://md.mova.elamur.ai/free/en/docs/papers/authors/omri-abend/) ×2
- Anita Gelboim
- Ehud Malul
- Oriel Perets
- Yigal Kassel
- Ziv Mor
- [← previous Eleni Miltsakaki](https://md.mova.elamur.ai/free/en/docs/papers/authors/eleni-miltsakaki/)
- [next → Elizabeth Coppock](https://md.mova.elamur.ai/free/en/docs/papers/authors/elizabeth-coppock/)
- [↑ back to Authors](https://md.mova.elamur.ai/free/en/docs/papers/authors/)