mlsanityData to 14 Oct 2026

ParaShoot: A Hebrew Question Answering Dataset

arXiv 2109.11314 ↗First posted 23 Sep 2021cs.CLcited by 12

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Abstract (arXiv)

NLP research in Hebrew has largely focused on morphology and syntax, where rich annotated datasets in the spirit of Universal Dependencies are available. Semantic datasets, however, are in short supply, hindering crucial advances in the development of NLP technology in Hebrew. In this work, we present ParaShoot, the first question answering dataset in modern Hebrew. The dataset follows the format and crowdsourcing methodology of SQuAD, and contains approximately 3000 annotated examples, similar to other question-answering datasets in low-resource languages. We provide the first baseline results using recently-released BERT-style models for Hebrew, showing that there is significant room for improvement on this task.

Key facts. Cited by 12 papers in the map (data through 14 Oct 2026). Builds on 14 of its 19 references that are in the map. First posted on arXiv 23 Sep 2021.

Builds on

14 of 19 references.

References · 14

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2016SQuAD: 100,000+ Questions for Machine Comprehension of Textcited by 4,148
  3. 2018Annotation Artifacts in Natural Language Inference Datacited by 671
  4. 2020TyDi QA: A Benchmark for Information-Seeking Question Answering in Typologically Diverse Languagescited by 488
  5. 2020A Monolingual Approach to Contextualized Word Embeddings for Mid-Resource Languagescited by 131
  6. 2018How Much Reading Does Reading Comprehension Require? A Critical Investigation of Popular Benchmarkscited by 88
  7. 2021Few-Shot Question Answering by Pretraining Span Selectioncited by 62
  8. 2020FQuAD: French Question Answering Datasetcited by 58
  9. 2019Neural Arabic Question Answeringcited by 48
  10. 2019KorQuAD1.0: Korean QA Dataset for Machine Reading Comprehensioncited by 43
  11. 2021AlephBERT:A Hebrew Large Pre-Trained Language Model to Start-off your Hebrew NLP Application Withcited by 28
  12. 2020From SPMRL to NMRL: What Did We Learn (and Unlearn) in a Decade of Parsing Morphologically-Rich Languages (MRLs)?cited by 26
2 more
  1. 2020Neural Modeling for Named Entities and Morphology (NEMO^2)cited by 26
  2. 2021HeBERT & HebEMO: a Hebrew BERT Model and a Tool for Polarity Analysis and Emotion Recognitioncited by 14

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Cited by year

Citing papers per year: 2021: 1; 2022: 2; 2023: 3; 2024: 5; 2025: 1.

12 papers.

  1. 2022Breaking Character: Are Subwords Good Enough for MRLs After All?cited by 13
  2. 2023HeRo: RoBERTa and Longformer Hebrew Language Modelscited by 12
  3. 2024RoQLlama: A Lightweight Romanian Adapted Language Modelcited by 7
  4. 2024HeSum: a Novel Dataset for Abstractive Text Summarization in Hebrewcited by 6
  5. 2021A Survey on non-English Question Answering Datasetcited by 5
  6. 2023Explicit Morphological Knowledge Improves Pre-training of Language Models for Hebrewcited by 5
  7. 2022Multilingual Sequence-to-Sequence Models for Hebrew NLPcited by 3
  8. 2025HeQ: a Large and Diverse Hebrew Reading Comprehension Benchmarkcited by 2
  9. 2023Introducing DictaLM -- A Large Generative Language Model for Modern Hebrewcited by 1
  10. 2024Evaluation Methodology for Large Language Models for Multilingual Document Question and Answercited by 0
  11. 2024A Dataset for Metaphor Detection in Early Medieval Hebrew Poetrycited by 0
  12. 2024Mevaker: Conclusion Extraction and Allocation Resources for the Hebrew Languagecited by 0