# A Survey on non-English Question Answering Dataset

arXiv: https://arxiv.org/abs/2112.13634 · HTML: https://mlsanity.com/paper/2112.13634/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 38 of its 59 references that are in the map.
- First posted on arXiv 27 Dec 2021.

## Builds on (most cited first, 30 of 38)

- [Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge](https://mlsanity.com/paper/1803.05457/) (2018)
- [SQuAD: 100,000+ Questions for Machine Comprehension of Text](https://mlsanity.com/paper/1606.05250/) (2016)
- [MathQA: Towards Interpretable Math Word Problem Solving with Operation-Based Formalisms](https://mlsanity.com/paper/1905.13319/) (2019)
- [TyDi QA: A Benchmark for Information-Seeking Question Answering in Typologically Diverse Languages](https://mlsanity.com/paper/2003.05002/) (2020)
- [MLQA: Evaluating Cross-lingual Extractive Question Answering](https://mlsanity.com/paper/1910.07475/) (2019)
- [A Survey on Recent Approaches for Natural Language Processing in Low-Resource Scenarios](https://mlsanity.com/paper/2010.12309/) (2020)
- [MKQA: A Linguistically Diverse Benchmark for Multilingual Open Domain Question Answering](https://mlsanity.com/paper/2007.15207/) (2020)
- [XOR QA: Cross-lingual Open-Retrieval Question Answering](https://mlsanity.com/paper/2010.11856/) (2020)
- [DuReader: a Chinese Machine Reading Comprehension Dataset from Real-world Applications](https://mlsanity.com/paper/1711.05073/) (2017)
- [The Goldilocks Principle: Reading Children's Books with Explicit Memory Representations](https://mlsanity.com/paper/1511.02301/) (2015)
- [QA Dataset Explosion: A Taxonomy of NLP Resources for Question Answering and Reading Comprehension](https://mlsanity.com/paper/2107.12708/) (2021)
- [A Span-Extraction Dataset for Chinese Machine Reading Comprehension](https://mlsanity.com/paper/1810.07366/) (2018)
- [FQuAD: French Question Answering Dataset](https://mlsanity.com/paper/2002.06071/) (2020)
- [DRCD: a Chinese Machine Reading Comprehension Dataset](https://mlsanity.com/paper/1806.00920/) (2018)
- [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)
- [ParsiNLU: A Suite of Language Understanding Challenges for Persian](https://mlsanity.com/paper/2012.06154/) (2020)
- [A Survey on Machine Reading Comprehension: Tasks, Evaluation Metrics and Benchmark Datasets](https://mlsanity.com/paper/2006.11880/) (2020)
- [GermanQuAD and GermanDPR: Improving Non-English Question Answering and Passage Retrieval](https://mlsanity.com/paper/2104.12741/) (2021)
- [SberQuAD -- Russian Reading Comprehension Dataset: Description and Analysis](https://mlsanity.com/paper/1912.09723/) (2019)
- [A Vietnamese Dataset for Evaluating Machine Reading Comprehension](https://mlsanity.com/paper/2009.14725/) (2020)
- [Large-scale Cloze Test Dataset Created by Teachers](https://mlsanity.com/paper/1711.03225/) (2017)
- [English Machine Reading Comprehension Datasets: A Survey](https://mlsanity.com/paper/2101.10421/) (2021)
- [A Simple Yet Strong Pipeline for HotpotQA](https://mlsanity.com/paper/2004.06753/) (2020)
- [New Vietnamese Corpus for Machine Reading Comprehension of Health News Articles](https://mlsanity.com/paper/2006.11138/) (2020)
- [Disfl-QA: A Benchmark Dataset for Understanding Disfluencies in Question Answering](https://mlsanity.com/paper/2106.04016/) (2021)
- [BiPaR: A Bilingual Parallel Dataset for Multilingual and Cross-lingual Reading Comprehension on Novels](https://mlsanity.com/paper/1910.05040/) (2019)
- [LiveQA: A Question Answering Dataset over Sports Live](https://mlsanity.com/paper/2010.00526/) (2020)
- [RuBQ: A Russian Dataset for Question Answering over Wikidata](https://mlsanity.com/paper/2005.10659/) (2020)
- [NOAHQA: Numerical Reasoning with Interpretable Graph Question Answering Dataset](https://mlsanity.com/paper/2109.10604/) (2021)

## Built on by (most cited first, 5 of 5)

- [CaLMQA: Exploring culturally specific long-form question answering across 23 languages](https://mlsanity.com/paper/2406.17761/) (2024)
- [Resources and Few-shot Learners for In-context Learning in Slavic Languages](https://mlsanity.com/paper/2304.01922/) (2023)
- [PCoQA: Persian Conversational Question Answering Dataset](https://mlsanity.com/paper/2312.04362/) (2023)
- [Building Efficient and Effective OpenQA Systems for Low-Resource Languages](https://mlsanity.com/paper/2401.03590/) (2024)
- [EuSQuAD: Automatically Translated and Aligned SQuAD2.0 for Basque](https://mlsanity.com/paper/2404.12177/) (2024)

## Abstract (arXiv)

Research in question answering datasets and models has gained a lot of attention in the research community. Many of them release their own question answering datasets as well as the models. There is tremendous progress that we have seen in this area of research. The aim of this survey is to recognize, summarize and analyze the existing datasets that have been released by many researchers, especially in non-English datasets as well as resources such as research code, and evaluation metrics. In this paper, we review question answering datasets that are available in common languages other than English such as French, German, Japanese, Chinese, Arabic, Russian, as well as the multilingual and cross-lingual question-answering datasets.