# ParaShoot: A Hebrew Question Answering Dataset

arXiv: https://arxiv.org/abs/2109.11314 · HTML: https://mlsanity.com/paper/2109.11314/

## 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 (most cited first, 14 of 14)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [SQuAD: 100,000+ Questions for Machine Comprehension of Text](https://mlsanity.com/paper/1606.05250/) (2016)
- [Annotation Artifacts in Natural Language Inference Data](https://mlsanity.com/paper/1803.02324/) (2018)
- [TyDi QA: A Benchmark for Information-Seeking Question Answering in Typologically Diverse Languages](https://mlsanity.com/paper/2003.05002/) (2020)
- [A Monolingual Approach to Contextualized Word Embeddings for Mid-Resource Languages](https://mlsanity.com/paper/2006.06202/) (2020)
- [How Much Reading Does Reading Comprehension Require? A Critical Investigation of Popular Benchmarks](https://mlsanity.com/paper/1808.04926/) (2018)
- [Few-Shot Question Answering by Pretraining Span Selection](https://mlsanity.com/paper/2101.00438/) (2021)
- [FQuAD: French Question Answering Dataset](https://mlsanity.com/paper/2002.06071/) (2020)
- [Neural Arabic Question Answering](https://mlsanity.com/paper/1906.05394/) (2019)
- [KorQuAD1.0: Korean QA Dataset for Machine Reading Comprehension](https://mlsanity.com/paper/1909.07005/) (2019)
- [AlephBERT:A Hebrew Large Pre-Trained Language Model to Start-off your Hebrew NLP Application With](https://mlsanity.com/paper/2104.04052/) (2021)
- [From SPMRL to NMRL: What Did We Learn (and Unlearn) in a Decade of Parsing Morphologically-Rich Languages (MRLs)?](https://mlsanity.com/paper/2005.01330/) (2020)
- [Neural Modeling for Named Entities and Morphology (NEMO^2)](https://mlsanity.com/paper/2007.15620/) (2020)
- [HeBERT & HebEMO: a Hebrew BERT Model and a Tool for Polarity Analysis and Emotion Recognition](https://mlsanity.com/paper/2102.01909/) (2021)

## Built on by (most cited first, 12 of 12)

- [Breaking Character: Are Subwords Good Enough for MRLs After All?](https://mlsanity.com/paper/2204.04748/) (2022)
- [HeRo: RoBERTa and Longformer Hebrew Language Models](https://mlsanity.com/paper/2304.11077/) (2023)
- [RoQLlama: A Lightweight Romanian Adapted Language Model](https://mlsanity.com/paper/2410.04269/) (2024)
- [HeSum: a Novel Dataset for Abstractive Text Summarization in Hebrew](https://mlsanity.com/paper/2406.03897/) (2024)
- [A Survey on non-English Question Answering Dataset](https://mlsanity.com/paper/2112.13634/) (2021)
- [Explicit Morphological Knowledge Improves Pre-training of Language Models for Hebrew](https://mlsanity.com/paper/2311.00658/) (2023)
- [Multilingual Sequence-to-Sequence Models for Hebrew NLP](https://mlsanity.com/paper/2212.09682/) (2022)
- [HeQ: a Large and Diverse Hebrew Reading Comprehension Benchmark](https://mlsanity.com/paper/2508.01812/) (2025)
- [Introducing DictaLM -- A Large Generative Language Model for Modern Hebrew](https://mlsanity.com/paper/2309.14568/) (2023)
- [Evaluation Methodology for Large Language Models for Multilingual Document Question and Answer](https://mlsanity.com/paper/2402.01065/) (2024)
- [A Dataset for Metaphor Detection in Early Medieval Hebrew Poetry](https://mlsanity.com/paper/2402.17371/) (2024)
- [Mevaker: Conclusion Extraction and Allocation Resources for the Hebrew Language](https://mlsanity.com/paper/2403.09719/) (2024)

## 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.