mlsanityData to 7 Oct 2026

PQuAD: A Persian Question Answering Dataset

arXiv 2202.06219 ↗First posted 13 Feb 2022cs.CL cs.IR cs.LGcited by 14

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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.

Key facts. Cited by 14 papers in the map (data through 7 Oct 2026). Builds on 22 of its 30 references that are in the map. First posted on arXiv 13 Feb 2022.

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22 of 30 references.

References · 22

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2016SQuAD: 100,000+ Questions for Machine Comprehension of Textcited by 4,115
  3. 2019Unsupervised Cross-lingual Representation Learning at Scalecited by 3,907
  4. 2019XLNet: Generalized Autoregressive Pretraining for Language Understandingcited by 2,801
  5. 2018Know What You Don't Know: Unanswerable Questions for SQuADcited by 1,538
  6. 2019SpanBERT: Improving Pre-training by Representing and Predicting Spanscited by 832
  7. 2017Zero-Shot Relation Extraction via Reading Comprehensioncited by 425
  8. 2016NewsQA: A Machine Comprehension Datasetcited by 330
  9. 2020LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attentioncited by 326
  10. 2017SearchQA: A New Q&A Dataset Augmented with Context from a Search Enginecited by 221
  11. 2018ReCoRD: Bridging the Gap between Human and Machine Commonsense Reading Comprehensioncited by 198
  12. 2015The Goldilocks Principle: Reading Children's Books with Explicit Memory Representationscited by 108
10 more
  1. 2020ParsBERT: Transformer-based Model for Persian Language Understandingcited by 92
  2. 2020FQuAD: French Question Answering Datasetcited by 58
  3. 2019Neural Arabic Question Answeringcited by 48
  4. 2019KorQuAD1.0: Korean QA Dataset for Machine Reading Comprehensioncited by 43
  5. 2019Automatic Spanish Translation of the SQuAD Dataset for Multilingual Question Answeringcited by 35
  6. 2020Text-based Question Answering from Information Retrieval and Deep Neural Network Perspectives: A Surveycited by 21
  7. 2020ReCO: A Large Scale Chinese Reading Comprehension Dataset on Opinioncited by 16
  8. 2016Embracing data abundance: BookTest Dataset for Reading Comprehensioncited by 15
  9. 2018A Question-Focused Multi-Factor Attention Network for Question Answeringcited by 7
  10. 2018Densely Connected Attention Propagation for Reading Comprehensioncited by 7

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Citing papers per year: 2023: 4; 2024: 4; 2025: 4; 2026: 2.

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