# SberQuAD -- Russian Reading Comprehension Dataset: Description and Analysis

arXiv: https://arxiv.org/abs/1912.09723 · HTML: https://mlsanity.com/paper/1912.09723/

## Key facts

- Cited by 38 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Dec 2019.

## Built on by (most cited first, 30 of 38)

- [The Belebele Benchmark: a Parallel Reading Comprehension Dataset in 122 Language Variants](https://mlsanity.com/paper/2308.16884/) (2023)
- [QA Dataset Explosion: A Taxonomy of NLP Resources for Question Answering and Reading Comprehension](https://mlsanity.com/paper/2107.12708/) (2021)
- [GermanQuAD and GermanDPR: Improving Non-English Question Answering and Passage Retrieval](https://mlsanity.com/paper/2104.12741/) (2021)
- [Generative Language Models for Paragraph-Level Question Generation](https://mlsanity.com/paper/2210.03992/) (2022)
- [English Machine Reading Comprehension Datasets: A Survey](https://mlsanity.com/paper/2101.10421/) (2021)
- [An Efficient Multilingual Language Model Compression through Vocabulary Trimming](https://mlsanity.com/paper/2305.15020/) (2023)
- [The Russian-focused embedders' exploration: ruMTEB benchmark and Russian embedding model design](https://mlsanity.com/paper/2408.12503/) (2024)
- [ReadMe++: Benchmarking Multilingual Language Models for Multi-Domain Readability Assessment](https://mlsanity.com/paper/2305.14463/) (2023)
- [TAPE: Assessing Few-shot Russian Language Understanding](https://mlsanity.com/paper/2210.12813/) (2022)
- [A Practical Toolkit for Multilingual Question and Answer Generation](https://mlsanity.com/paper/2305.17416/) (2023)
- [Sentence Extraction-Based Machine Reading Comprehension for Vietnamese](https://mlsanity.com/paper/2105.09043/) (2021)
- [Learn Your Tokens: Word-Pooled Tokenization for Language Modeling](https://mlsanity.com/paper/2310.11628/) (2023)
- [MultiWikiQA: A Reading Comprehension Benchmark in 300+ Languages](https://mlsanity.com/paper/2509.04111/) (2025)
- [JaQuAD: Japanese Question Answering Dataset for Machine Reading Comprehension](https://mlsanity.com/paper/2202.01764/) (2022)
- [VLSP 2021 - ViMRC Challenge: Vietnamese Machine Reading Comprehension](https://mlsanity.com/paper/2203.11400/) (2022)
- [Knowledge Distillation of Russian Language Models with Reduction of Vocabulary](https://mlsanity.com/paper/2205.02340/) (2022)
- [NorQuAD: Norwegian Question Answering Dataset](https://mlsanity.com/paper/2305.01957/) (2023)
- [KazQAD: Kazakh Open-Domain Question Answering Dataset](https://mlsanity.com/paper/2404.04487/) (2024)
- [UQuAD1.0: Development of an Urdu Question Answering Training Data for Machine Reading Comprehension](https://mlsanity.com/paper/2111.01543/) (2021)
- [Resources and Few-shot Learners for In-context Learning in Slavic Languages](https://mlsanity.com/paper/2304.01922/) (2023)
- [A Survey on non-English Question Answering Dataset](https://mlsanity.com/paper/2112.13634/) (2021)
- [TIGQA:An Expert Annotated Question Answering Dataset in Tigrinya](https://mlsanity.com/paper/2404.17194/) (2024)
- [Beyond Early-Token Bias: Model-Specific and Language-Specific Position Effects in Multilingual LLMs](https://mlsanity.com/paper/2505.16134/) (2025)
- [MOLE: Metadata Extraction and Validation in Scientific Papers Using LLMs](https://mlsanity.com/paper/2505.19800/) (2025)
- [DTW at Qur'an QA 2022: Utilising Transfer Learning with Transformers for Question Answering in a Low-resource Domain](https://mlsanity.com/paper/2205.06025/) (2022)
- [A Russian Jeopardy! Data Set for Question-Answering Systems](https://mlsanity.com/paper/2112.02325/) (2021)
- [ALoFTRAG: Automatic Local Fine Tuning for Retrieval Augmented Generation](https://mlsanity.com/paper/2501.11929/) (2025)
- [Native Chinese Reader: A Dataset Towards Native-Level Chinese Machine Reading Comprehension](https://mlsanity.com/paper/2112.06494/) (2021)
- [Russian SuperGLUE 1.1: Revising the Lessons not Learned by Russian NLP models](https://mlsanity.com/paper/2202.07791/) (2022)
- [XLMRQA: Open-Domain Question Answering on Vietnamese Wikipedia-based Textual Knowledge Source](https://mlsanity.com/paper/2204.07002/) (2022)

## Abstract (arXiv)

SberQuAD -- a large scale analog of Stanford SQuAD in the Russian language - is a valuable resource that has not been properly presented to the scientific community. We fill this gap by providing a description, a thorough analysis, and baseline experimental results.