[Mova](https://md.mova.elamur.ai/free/en/) · [this page in HTML](https://mova.elamur.ai/free/en/docs/papers/authors/gabriel-stanovsky/) [home](https://md.mova.elamur.ai/free/en/) / [docs](https://md.mova.elamur.ai/free/en/docs/) / [papers](https://md.mova.elamur.ai/free/en/docs/papers/) / [authors](https://md.mova.elamur.ai/free/en/docs/papers/authors/) / Gabriel Stanovsky # Gabriel Stanovsky 6 paper cards in Mova's library · 2018–2026 Gabriel Stanovsky wrote 6 of the papers Mova reads, in 2018–2026, most of them on [Semantic role labeling](https://md.mova.elamur.ai/free/en/docs/papers/topics/semantic-role-labeling/), [Akkadian and cuneiform NLP](https://md.mova.elamur.ai/free/en/docs/papers/topics/akkadian-cuneiform-nlp/); often with [Ido Dagan](https://md.mova.elamur.ai/free/en/docs/papers/authors/ido-dagan/), [Julian Michael](https://md.mova.elamur.ai/free/en/docs/papers/authors/julian-michael/), [Luke Zettlemoyer](https://md.mova.elamur.ai/free/en/docs/papers/authors/luke-zettlemoyer/). [OpenAlex](https://openalex.org/A5082136238) · [ORCID](https://orcid.org/0000-0002-2420-8979) · [**h-index 22**](https://openalex.org/authors/A5082136238) · [2605 citations](https://openalex.org/authors/A5082136238) · [140 works](https://openalex.org/works?filter=author.id:A5082136238) — 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=Gabriel+Stanovsky) · [Semantic Scholar](https://www.semanticscholar.org/search?q=Gabriel+Stanovsky) · [dblp](https://dblp.org/search?q=Gabriel+Stanovsky) · [ACL Anthology](https://aclanthology.org/search/?q=Gabriel+Stanovsky) · [ORCID](https://orcid.org/orcid-search/search?searchQuery=Gabriel+Stanovsky) Topics: [Semantic role labeling](https://md.mova.elamur.ai/free/en/docs/papers/topics/semantic-role-labeling/) 2 · [Akkadian and cuneiform NLP](https://md.mova.elamur.ai/free/en/docs/papers/topics/akkadian-cuneiform-nlp/) 1 ## Papers 1. **[Leveraging Digitized Newspapers to Collect Summarization Data in Low-Resource Languages](https://md.mova.elamur.ai/free/en/docs/papers/aclbulk-2026-dahan-leveraging-digitized-newspapers-collect-summarization/)** *(2026)* Front-page teasers are short blurbs that a newspaper's editors write on the first page to point to full articles inside the issue. 2. **[Filling the Gaps in Ancient Akkadian Texts: A Masked Language Modelling Approach](https://md.mova.elamur.ai/free/en/docs/papers/emnlp-2021-lazar-filling-gaps-ancient-akkadian-texts/)** *(2021)* Editors of damaged cuneiform tablets fill broken parts from context, and the authors treat this as masked language modelling over Oracc transliterations (about 1M words, 2.3M signs). 3. **[Automated Extraction of Sentencing Decisions from Court Cases in the Hebrew Language](https://md.mova.elamur.ai/free/en/docs/papers/aclbulk-2021-wenger-automated-extraction-sentencing-decisions-court/)** *(2021)* The paper defines Automated Punishment Extraction (APE): from an Israeli court sentencing decision in a sexual-offence case, find the sentence that states the actual imprisonment and give its length in months. 4. **[Controlled Crowdsourcing for High-Quality QA-SRL Annotation](https://md.mova.elamur.ai/free/en/docs/papers/roit-2020-controlled-crowdsourcing-high-quality-qa/)** *(2020)* The paper shows that the crowdsourced QA-SRL data of 2018 has low coverage, with an estimated recall in the low 70s, and proposes a controlled protocol to fix it. 5. **[DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs](https://md.mova.elamur.ai/free/en/docs/papers/dua-2019-drop-reading-comprehension-benchmark-requiring/)** *(2019)* DROP has 96,567 questions on about 7,000 Wikipedia passages rich in numbers (American-football game summaries, history), written adversarially against a reading model so that answering needs discrete operations over several places in the text. 6. **[Crowdsourcing Question-Answer Meaning Representations](https://md.mova.elamur.ai/free/en/docs/papers/michael-2018-crowdsourcing-question-answer-meaning-representations/)** *(2018)* A QAMR represents a sentence's predicate-argument structure as a set of free-form wh-question / answer-span pairs about all its content words, not only verbs («How old is Pierre Vinken? – 61 years old», «What will he join the board as? – nonexecutive director»). ## Co-authors (25) - [Ido Dagan](https://md.mova.elamur.ai/free/en/docs/papers/authors/ido-dagan/) ×2 - [Julian Michael](https://md.mova.elamur.ai/free/en/docs/papers/authors/julian-michael/) ×2 - [Luke Zettlemoyer](https://md.mova.elamur.ai/free/en/docs/papers/authors/luke-zettlemoyer/) ×2 - Asaf Yehudai - [Ayal Klein](https://md.mova.elamur.ai/free/en/docs/papers/authors/ayal-klein/) - Benny Saret - Carmit Klar Chalamish - [Daniela Stepanov](https://md.mova.elamur.ai/free/en/docs/papers/authors/daniela-stepanov/) - [Dheeru Dua](https://md.mova.elamur.ai/free/en/docs/papers/authors/dheeru-dua/) - [Jonathan Mamou](https://md.mova.elamur.ai/free/en/docs/papers/authors/jonathan-mamou/) - Koren Lazar - [Luheng He](https://md.mova.elamur.ai/free/en/docs/papers/authors/luheng-he/) - [Matt Gardner](https://md.mova.elamur.ai/free/en/docs/papers/authors/matt-gardner/) - Mohr Wenger - Nathan Wasserman - Noam Dahan - Noga Berger - Omer Kidron - Paul Roit - [Pradeep Dasigi](https://md.mova.elamur.ai/free/en/docs/papers/authors/pradeep-dasigi/) - Renana Keydar - [Sameer Singh](https://md.mova.elamur.ai/free/en/docs/papers/authors/sameer-singh/) - Tom Kalir - Wayne Horowitz - Yizhong Wang - [← previous Gabor Bella](https://md.mova.elamur.ai/free/en/docs/papers/authors/gabor-bella/) - [next → Gema Ramírez-Sánchez](https://md.mova.elamur.ai/free/en/docs/papers/authors/gema-ramirez-sanchez/) - [↑ back to Authors](https://md.mova.elamur.ai/free/en/docs/papers/authors/)