# PQuAD: A Persian Question Answering Dataset

arXiv: https://arxiv.org/abs/2202.06219 · HTML: https://mlsanity.com/paper/2202.06219/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 30 references that are in the map.
- First posted on arXiv 13 Feb 2022.

## Builds on (most cited first, 22 of 22)

- [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)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://mlsanity.com/paper/1906.08237/) (2019)
- [Know What You Don't Know: Unanswerable Questions for SQuAD](https://mlsanity.com/paper/1806.03822/) (2018)
- [SpanBERT: Improving Pre-training by Representing and Predicting Spans](https://mlsanity.com/paper/1907.10529/) (2019)
- [Zero-Shot Relation Extraction via Reading Comprehension](https://mlsanity.com/paper/1706.04115/) (2017)
- [NewsQA: A Machine Comprehension Dataset](https://mlsanity.com/paper/1611.09830/) (2016)
- [LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention](https://mlsanity.com/paper/2010.01057/) (2020)
- [SearchQA: A New Q&A Dataset Augmented with Context from a Search Engine](https://mlsanity.com/paper/1704.05179/) (2017)
- [ReCoRD: Bridging the Gap between Human and Machine Commonsense Reading Comprehension](https://mlsanity.com/paper/1810.12885/) (2018)
- [The Goldilocks Principle: Reading Children's Books with Explicit Memory Representations](https://mlsanity.com/paper/1511.02301/) (2015)
- [ParsBERT: Transformer-based Model for Persian Language Understanding](https://mlsanity.com/paper/2005.12515/) (2020)
- [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)
- [Automatic Spanish Translation of the SQuAD Dataset for Multilingual Question Answering](https://mlsanity.com/paper/1912.05200/) (2019)
- [Text-based Question Answering from Information Retrieval and Deep Neural Network Perspectives: A Survey](https://mlsanity.com/paper/2002.06612/) (2020)
- [ReCO: A Large Scale Chinese Reading Comprehension Dataset on Opinion](https://mlsanity.com/paper/2006.12146/) (2020)
- [Embracing data abundance: BookTest Dataset for Reading Comprehension](https://mlsanity.com/paper/1610.00956/) (2016)
- [A Question-Focused Multi-Factor Attention Network for Question Answering](https://mlsanity.com/paper/1801.08290/) (2018)
- [Densely Connected Attention Propagation for Reading Comprehension](https://mlsanity.com/paper/1811.04210/) (2018)

## Built on by (most cited first, 14 of 14)

- [Khayyam Challenge (PersianMMLU): Is Your LLM Truly Wise to The Persian Language?](https://mlsanity.com/paper/2404.06644/) (2024)
- [F2LLM-v2: Inclusive, Performant, and Efficient Embeddings for a Multilingual World](https://mlsanity.com/paper/2603.19223/) (2026)
- [FaBERT: Pre-training BERT on Persian Blogs](https://mlsanity.com/paper/2402.06617/) (2024)
- [PerCul: A Story-Driven Cultural Evaluation of LLMs in Persian](https://mlsanity.com/paper/2502.07459/) (2025)
- [TookaBERT: A Step Forward for Persian NLU](https://mlsanity.com/paper/2407.16382/) (2024)
- [ViMMRC 2.0 -- Enhancing Machine Reading Comprehension on Vietnamese Literature Text](https://mlsanity.com/paper/2303.18162/) (2023)
- [PCoQA: Persian Conversational Question Answering Dataset](https://mlsanity.com/paper/2312.04362/) (2023)
- [A Persian Benchmark for Joint Intent Detection and Slot Filling](https://mlsanity.com/paper/2303.00408/) (2023)
- [Advancing Retrieval-Augmented Generation for Persian: Development of Language Models, Comprehensive Benchmarks, and Best Practices for Optimization](https://mlsanity.com/paper/2501.04858/) (2025)
- [IslamicPCQA: A Dataset for Persian Multi-hop Complex Question Answering in Islamic Text Resources](https://mlsanity.com/paper/2304.11664/) (2023)
- [Building a Rich Dataset to Empower the Persian Question Answering Systems](https://mlsanity.com/paper/2412.20212/) (2024)
- [PerHalluEval: Persian Hallucination Evaluation Benchmark for Large Language Models](https://mlsanity.com/paper/2509.21104/) (2025)
- [FarsiMCQGen: a Persian Multiple-choice Question Generation Framework](https://mlsanity.com/paper/2510.15134/) (2025)
- [IHUBERT: Vector-Based Semantic Deduplication and Domain-Balanced Pretraining for Persian Resources](https://mlsanity.com/paper/2606.20089/) (2026)

## Abstract (arXiv)

We present Persian Question Answering Dataset (PQuAD), a crowdsourced reading comprehension dataset on Persian Wikipedia articles. It includes 80,000 questions along with their answers, with 25% of the questions being adversarially unanswerable. We examine various properties of the dataset to show the diversity and the level of its difficulty as an MRC benchmark. By releasing this dataset, we aim to ease research on Persian reading comprehension and development of Persian question answering systems. Our experiments on different state-of-the-art pre-trained contextualized language models show 74.8% Exact Match (EM) and 87.6% F1-score that can be used as the baseline results for further research on Persian QA.