--- title: "Reut Tsarfaty — papers, page 2 — Elamur Research" description: "Reut Tsarfaty, Google: 44 papers on Elamur Research (2006–2026), h-index 24 — affiliations, topics, co-authors and who cites them in our library." canonical: "https://research.elamur.ai/free/en/authors/reut-tsarfaty/page/2/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/authors/reut-tsarfaty/page/2/) [home](https://md.research.elamur.ai/free/en/) / [authors](https://md.research.elamur.ai/free/en/authors/) / [Reut Tsarfaty](https://md.research.elamur.ai/free/en/authors/reut-tsarfaty/) / page 2 # Reut Tsarfaty · page 2 44 papers in our library · 2006–2026 · page 2 of 2 [The profile, affiliations and co-authors →](https://md.research.elamur.ai/free/en/authors/reut-tsarfaty/) ## Papers The most cited first (OpenAlex and our library). [← prev](https://md.research.elamur.ai/free/en/authors/reut-tsarfaty/) [1](https://md.research.elamur.ai/free/en/authors/reut-tsarfaty/) **2** 26. **[Design Choices in Crowdsourcing Discourse Relation Annotations: The Effect of Worker Selection and Training](https://md.research.elamur.ai/free/en/papers/scholman-2022-design-choices-crowdsourcing-discourse-relation/)** *(2022 · 11 citations)* Getting lay crowdworkers to label implicit discourse relations is difficult. The task depends on interpretation, and PDTB 3.0 distinguishes 36 sense types that novices cannot realistically master. This paper asks how recruiting and preparing workers affects label quality, measured as agreement between worker labels… 27. **[Morphology Without Borders: Clause-Level Morphology](https://md.research.elamur.ai/free/en/papers/goldman-2022-morphology-without-borders-clause-level/)** *(2022 · 11 citations)* Supervised computational morphology relies mostly on UniMorph, whose inflection tables are organized around the idea of a "word." The authors show that this idea is applied inconsistently across languages. 28. **[Multilingual Sequence-to-Sequence Models for Hebrew NLP](https://md.research.elamur.ai/free/en/papers/eyal-2023-multilingual-sequence-sequence-models-hebrew/)** *(2023 · 9 citations)* The authors argue that Hebrew encoder-only models (AlephBERT, AlephBERT-Gimmel, HeBERT) cannot emit morphemes or labels that are not in the input, so they need extra segment-then-tag components. 29. **[Universal Morpho-Syntactic Parsing and the Contribution of Lexica: Analyzing the ONLP Lab Submission to the CoNLL 2018 Shared Task](https://md.research.elamur.ai/free/en/papers/seker-2018-universal-morpho-syntactic-parsing-contribution/)** *(2018 · 9 citations)* The ONLP Lab submission used UDPipe for segmentation and morphological disambiguation, then yap's arc-eager dependency parser (a Zhang–Nivre variant with morphological-signature features). 30. **[MRL Parsing Without Tears: The Case of Hebrew](https://md.research.elamur.ai/free/en/papers/shmidman-2024-mrl-parsing-without-tears-case/)** *(2024 · 8 citations)* 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. 31. **[A Truly Joint Neural Architecture for Segmentation and Parsing](https://md.research.elamur.ai/free/en/papers/levi-2024-truly-joint-neural-architecture-segmentation/)** *(2024 · 7 citations)* The paper argues that Hebrew segmentation and dependency parsing should be solved jointly, as pre-neural parsers did. 32. **[HeSum: a Novel Dataset for Abstractive Text Summarization in Hebrew](https://md.research.elamur.ai/free/en/papers/argaman-2024-hesum-novel-dataset-abstractive-text/)** *(2024 · 7 citations)* HeSum is a Modern Hebrew summarisation set of 10,000 article–summary pairs (8,000 train, 1,000 validation, 1,000 test) from three independent news sites, where the summary is the editor's extended subheading. 33. **[Explicit Morphological Knowledge Improves Pre-training of Language Models for Hebrew](https://md.research.elamur.ai/free/en/papers/gueta-2023-explicit-morphological-knowledge-improves-pre/)** *(2023 · 7 citations)* The question is whether a BERT-style Hebrew model learns better when its tokenizer is told about Hebrew morphology before pre-training, instead of a purely statistical WordPiece split. 34. **[A Novel Challenge Set for Hebrew Morphological Disambiguation and Diacritics Restoration](https://md.research.elamur.ai/free/en/papers/shmidman-2020-novel-challenge-set-hebrew-morphological/)** *(2020 · 6 citations)* 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… 35. **[MoNaCo: More Natural and Complex Questions for Reasoning Across Dozens of Documents](https://md.research.elamur.ai/free/en/papers/wolfson-2026-monaco-more-natural-complex-questions/)** *(2026 · 5 citations)* LLM-based agents are increasingly used to look up information. However, few benchmarks test them on realistic questions that would take a person hours of research across many sources. MoNaCo fills this gap with 1,315 human-written questions. Crowd workers wrote them while imagining the needs of specific personas… 36. **[Do Pretrained Contextual Language Models Distinguish between Hebrew Homograph Analyses?](https://md.research.elamur.ai/free/en/papers/shmidman-2023-do-pretrained-contextual-language-models/)** *(2023 · 5 citations)* Unpointed Hebrew is full of homographs whose analyses differ in segmentation (the prefix or suffix is or is not a word), in morphosyntax, or only in sense. 37. **[HeGeL: A Novel Dataset for Geo-Location from Hebrew Text](https://md.research.elamur.ai/free/en/papers/argaman-2023-hegel-novel-dataset-geo-location/)** *(2023 · 4 citations)* The task is to turn a free-text place description into map coordinates. The authors crowdsourced 5,649 literal Hebrew place descriptions for 167 places in Tel Aviv, Haifa and Jerusalem. Writers described a place they know without naming it or any landmark within 100 m; a second participant then marked it on… 38. **[Universal Joint Morph-Syntactic Processing: The Open University of Israel's Submission to The CoNLL 2017 Shared Task](https://md.research.elamur.ai/free/en/papers/more-2017-universal-joint-morph-syntactic-processing/)** *(2017 · 1 citation)* The OpenU-NLP-Lab system for the CoNLL 2017 UD shared task is a transition-based, non-neural joint morphological disambiguator and arc-standard dependency parser (built in yap). 39. **[Book Review: Design Patterns in Fluid Construction Grammar edited by Luc Steels](https://md.research.elamur.ai/free/en/papers/schneider-2013-book-review-design-patterns-fluid/)** *(2013 · 1 citation)* This is a review by Nathan Schneider and Reut Tsarfaty of a volume edited by Luc Steels on Fluid Construction Grammar (FCG). 40. **[Beyond Word Boundaries: A Hebrew Coreference Benchmark and an Evaluation Protocol for Morphologically Complex Text](https://md.research.elamur.ai/free/en/papers/greenfeld-2026-beyond-word-boundaries-hebrew-coreference/)** *(2026)* The paper builds KibutzR, a Modern Hebrew coreference dataset on the UD Hebrew treebank. The 6,151 sentences are regrouped into 351 documents, and any document that appears in more than one partition is moved wholly into training. Mentions are marked at word, morpheme and multiword level: a possessive suffix or… 41. **[From Benchmarks to Skills: Low-Rank Factors for LLM Evaluation](https://md.research.elamur.ai/free/en/papers/maimon-2026-benchmarks-skills-low-rank-factors/)** *(2026)* LLMs are usually compared through long lists of benchmark scores or a single averaged number. It is unclear what these numbers measure and how much benchmarks overlap. The authors borrow tools from psychometrics. They treat models as respondents and benchmarks as test items, then build a leaderboard of 60 LLMs scored… 42. **[MameLoshnLM: Yiddish Language Model and Evaluation Benchmark](https://md.research.elamur.ai/free/en/papers/katz-2026-mameloshnlm-yiddish-language-model-evaluation/)** *(2026)* Yiddish has a long written tradition but a small and unreliable presence on the web, so multilingual models handle it poorly and there are few trustworthy ways to measure them. 43. **[HeSum: a Novel Dataset for Abstractive Text Summarization in Hebrew](https://md.research.elamur.ai/free/en/papers/mondshine-2024-hesum-novel-dataset-abstractive-text/)** *(2024)* The paper builds HeSum, 10,000 Modern Hebrew news articles paired with the editor-written extended subheadings that serve as summaries, taken from three independent news sites and split 8,000 / 1,000 / 1,000. 44. **[The Truth, The Whole Truth, and Nothing but the Truth: A New Benchmark Dataset for Hebrew Text Credibility Assessment](https://md.research.elamur.ai/free/en/papers/hagag-2023-truth-whole-truth-nothing-but/)** *(2023)* The paper introduces HeTrue, 1,021 statements by Israeli politicians and public figures, collected and graded by the journalists of an IFCN fact-checking outlet. 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