mlsanityData to 14 Oct 2026

WikiOmnia: generative QA corpus on the whole Russian Wikipedia

arXiv 2204.08009 ↗First posted 17 Apr 2022cs.CL cs.AIcited by 2

Not on the map. Why?

Abstract (arXiv)

The General QA field has been developing the methodology referencing the Stanford Question answering dataset (SQuAD) as the significant benchmark. However, compiling factual questions is accompanied by time- and labour-consuming annotation, limiting the training data's potential size. We present the WikiOmnia dataset, a new publicly available set of QA-pairs and corresponding Russian Wikipedia article summary sections, composed with a fully automated generative pipeline. The dataset includes every available article from Wikipedia for the Russian language. The WikiOmnia pipeline is available open-source and is also tested for creating SQuAD-formatted QA on other domains, like news texts, fiction, and social media. The resulting dataset includes two parts: raw data on the whole Russian Wikipedia (7,930,873 QA pairs with paragraphs for ruGPT-3 XL and 7,991,040 QA pairs with paragraphs for ruT5-large) and cleaned data with strict automatic verification (over 160,000 QA pairs with paragraphs for ruGPT-3 XL and over 3,400,000 QA pairs with paragraphs for ruT5-large).

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 33 of its 46 references that are in the map. First posted on arXiv 17 Apr 2022.

Builds on

33 of 46 references.

References · 33

  1. 2020Language Models are Few-Shot Learnerscited by 32,419
  2. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,599
  3. 2016SQuAD: 100,000+ Questions for Machine Comprehension of Textcited by 4,148
  4. 2021Finetuned Language Models Are Zero-Shot Learnerscited by 3,780
  5. 2017TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehensioncited by 2,955
  6. 2019The Curious Case of Neural Text Degenerationcited by 2,916
  7. 2019Language Models as Knowledge Bases?cited by 1,936
  8. 2018Know What You Don't Know: Unanswerable Questions for SQuADcited by 1,554
  9. 2019How Can We Know What Language Models Know?cited by 959
  10. 2019On the Cross-lingual Transferability of Monolingual Representationscited by 578
  11. 2018Texygen: A Benchmarking Platform for Text Generation Modelscited by 491
  12. 2020TyDi QA: A Benchmark for Information-Seeking Question Answering in Typologically Diverse Languagescited by 488
21 more
  1. 2021PAQ: 65 Million Probably-Asked Questions and What You Can Do With Themcited by 200
  2. 2021Generating Datasets with Pretrained Language Modelscited by 193
  3. 2017Learning to Ask: Neural Question Generation for Reading Comprehensioncited by 186
  4. 2020MKQA: A Linguistically Diverse Benchmark for Multilingual Open Domain Question Answeringcited by 153
  5. 2019Synthetic QA Corpora Generation with Roundtrip Consistencycited by 143
  6. 2020XOR QA: Cross-lingual Open-Retrieval Question Answeringcited by 121
  7. 2020X-FACTR: Multilingual Factual Knowledge Retrieval from Pretrained Language Modelscited by 117
  8. 2021Knowledgeable or Educated Guess? Revisiting Language Models as Knowledge Basescited by 107
  9. 2020Training Question Answering Models From Synthetic Datacited by 95
  10. 2020Language Models are Open Knowledge Graphscited by 95
  11. 2021QA Dataset Explosion: A Taxonomy of NLP Resources for Question Answering and Reading Comprehensioncited by 91
  12. 2019Inducing Relational Knowledge from BERTcited by 75
  13. 2020End-to-End Synthetic Data Generation for Domain Adaptation of Question Answering Systemscited by 65
  14. 2021Can Generative Pre-trained Language Models Serve as Knowledge Bases for Closed-book QA?cited by 62
  15. 2020Asking Questions the Human Way: Scalable Question-Answer Generation from Text Corpuscited by 40
  16. 2019SberQuAD -- Russian Reading Comprehension Dataset: Description and Analysiscited by 38
  17. 2020BERT-kNN: Adding a kNN Search Component to Pretrained Language Models for Better QAcited by 36
  18. 2020Generating Diverse and Consistent QA pairs from Contexts with Information-Maximizing Hierarchical Conditional VAEscited by 31
  19. 2021Improving Unsupervised Question Answering via Summarization-Informed Question Generationcited by 21
  20. 2020Dataset for Automatic Summarization of Russian Newscited by 12
  21. 2021OneStop QAMaker: Extract Question-Answer Pairs from Text in a One-Stop Approachcited by 7

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 0; 2026: 1.

2 papers.

  1. 2024The Russian-focused embedders' exploration: ruMTEB benchmark and Russian embedding model designcited by 20
  2. 2026F2LLM-v2: Inclusive, Performant, and Efficient Embeddings for a Multilingual Worldcited by 12