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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, Akkadian and cuneiform NLP; often with Ido Dagan, Julian Michael, Luke Zettlemoyer.

OpenAlex · ORCID · h-index 22 · 2605 citations · 140 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: Semantic role labeling 2 · Akkadian and cuneiform NLP 1

Papers

  1. Leveraging Digitized Newspapers to Collect Summarization Data in Low-Resource Languages (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 (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 (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 (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 (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 (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)

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