# AfriQA: Cross-lingual Open-Retrieval Question Answering for African Languages

arXiv: https://arxiv.org/abs/2305.06897 · HTML: https://mlsanity.com/paper/2305.06897/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 62 references that are in the map.
- First posted on arXiv 11 May 2023.

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

- [SQuAD: 100,000+ Questions for Machine Comprehension of Text](https://mlsanity.com/paper/1606.05250/) (2016)
- [Dense Passage Retrieval for Open-Domain Question Answering](https://mlsanity.com/paper/2004.04906/) (2020)
- [TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension](https://mlsanity.com/paper/1705.03551/) (2017)
- [Can a Suit of Armor Conduct Electricity? A New Dataset for Open Book Question Answering](https://mlsanity.com/paper/1809.02789/) (2018)
- [mT5: A massively multilingual pre-trained text-to-text transformer](https://mlsanity.com/paper/2010.11934/) (2020)
- [MS MARCO: A Human Generated MAchine Reading COmprehension Dataset](https://mlsanity.com/paper/1611.09268/) (2016)
- [No Language Left Behind: Scaling Human-Centered Machine Translation](https://mlsanity.com/paper/2207.04672/) (2022)
- [On the Cross-lingual Transferability of Monolingual Representations](https://mlsanity.com/paper/1910.11856/) (2019)
- [FinQA: A Dataset of Numerical Reasoning over Financial Data](https://mlsanity.com/paper/2109.00122/) (2021)
- [Beyond English-Centric Multilingual Machine Translation](https://mlsanity.com/paper/2010.11125/) (2020)
- [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)
- [Participatory Research for Low-resourced Machine Translation: A Case Study in African Languages](https://mlsanity.com/paper/2010.02353/) (2020)
- [MasakhaNER: Named Entity Recognition for African Languages](https://mlsanity.com/paper/2103.11811/) (2021)
- [MKQA: A Linguistically Diverse Benchmark for Multilingual Open Domain Question Answering](https://mlsanity.com/paper/2007.15207/) (2020)
- [Adapting Pre-trained Language Models to African Languages via Multilingual Adaptive Fine-Tuning](https://mlsanity.com/paper/2204.06487/) (2022)
- [XOR QA: Cross-lingual Open-Retrieval Question Answering](https://mlsanity.com/paper/2010.11856/) (2020)
- [A Few Thousand Translations Go a Long Way! Leveraging Pre-trained Models for African News Translation](https://mlsanity.com/paper/2205.02022/) (2022)
- [FQuAD: French Question Answering Dataset](https://mlsanity.com/paper/2002.06071/) (2020)
- [Transfer Learning and Distant Supervision for Multilingual Transformer Models: A Study on African Languages](https://mlsanity.com/paper/2010.03179/) (2020)
- [Making a MIRACL: Multilingual Information Retrieval Across a Continuum of Languages](https://mlsanity.com/paper/2210.09984/) (2022)
- [AfroLID: A Neural Language Identification Tool for African Languages](https://mlsanity.com/paper/2210.11744/) (2022)
- [MasakhaNER 2.0: Africa-centric Transfer Learning for Named Entity Recognition](https://mlsanity.com/paper/2210.12391/) (2022)
- [MasakhaNEWS: News Topic Classification for African languages](https://mlsanity.com/paper/2304.09972/) (2023)
- [Towards Best Practices for Training Multilingual Dense Retrieval Models](https://mlsanity.com/paper/2204.02363/) (2022)
- [yosm: A new yoruba sentiment corpus for movie reviews](https://mlsanity.com/paper/2204.09711/) (2022)
- [KenSwQuAD -- A Question Answering Dataset for Swahili Low Resource Language](https://mlsanity.com/paper/2205.02364/) (2022)
- [A Survey on Legal Question Answering Systems](https://mlsanity.com/paper/2110.07333/) (2021)
- [TaTa: A Multilingual Table-to-Text Dataset for African Languages](https://mlsanity.com/paper/2211.00142/) (2022)
- [AmQA: Amharic Question Answering Dataset](https://mlsanity.com/paper/2303.03290/) (2023)

## Built on by (most cited first, 23 of 23)

- [Aya Model: An Instruction Finetuned Open-Access Multilingual Language Model](https://mlsanity.com/paper/2402.07827/) (2024)
- [Aya Dataset: An Open-Access Collection for Multilingual Instruction Tuning](https://mlsanity.com/paper/2402.06619/) (2024)
- [AfroBench: How Good are Large Language Models on African Languages?](https://mlsanity.com/paper/2311.07978/) (2023)
- [MEXA: Multilingual Evaluation of English-Centric LLMs via Cross-Lingual Alignment](https://mlsanity.com/paper/2410.05873/) (2024)
- [Multilingual Large Language Models: A Systematic Survey](https://mlsanity.com/paper/2411.11072/) (2024)
- [Charting the Landscape of African NLP: Mapping Progress and Shaping the Road Ahead](https://mlsanity.com/paper/2505.21315/) (2025)
- [Teaching LLMs to Abstain across Languages via Multilingual Feedback](https://mlsanity.com/paper/2406.15948/) (2024)
- [Uhura: A Benchmark for Evaluating Scientific Question Answering and Truthfulness in Low-Resource African Languages](https://mlsanity.com/paper/2412.00948/) (2024)
- [NusaWrites: Constructing High-Quality Corpora for Underrepresented and Extremely Low-Resource Languages](https://mlsanity.com/paper/2309.10661/) (2023)
- [OpenLID-v3: Improving the Precision of Closely Related Language Identification -- An Experience Report](https://mlsanity.com/paper/2602.13139/) (2026)
- [NaijaRC: A Multi-choice Reading Comprehension Dataset for Nigerian Languages](https://mlsanity.com/paper/2308.09768/) (2023)
- [Bridging Gaps in Natural Language Processing for Yor`ub'a: A Systematic Review of a Decade of Progress and Prospects](https://mlsanity.com/paper/2502.17364/) (2025)
- [HausaNLP: Current Status, Challenges and Future Directions for Hausa Natural Language Processing](https://mlsanity.com/paper/2505.14311/) (2025)
- [KinyaColBERT: A Lexically Grounded Retrieval Model for Low-Resource Retrieval-Augmented Generation](https://mlsanity.com/paper/2507.03241/) (2025)
- [Afri-MCQA: Multimodal Cultural Question Answering for African Languages](https://mlsanity.com/paper/2601.05699/) (2026)
- [Machine Intelligence in Africa: a survey](https://mlsanity.com/paper/2402.02218/) (2024)
- [M4-RAG: A Massive-Scale Multilingual Multi-Cultural Multimodal RAG](https://mlsanity.com/paper/2512.05959/) (2025)
- [GaelEval: Benchmarking LLM Performance for Scottish Gaelic](https://mlsanity.com/paper/2604.02135/) (2026)
- [Script-Agnostic Language Identification](https://mlsanity.com/paper/2406.17901/) (2024)
- [WolBanking77: Wolof Banking Speech Intent Classification Dataset](https://mlsanity.com/paper/2509.19271/) (2025)
- [Opportunities and Challenges of Natural Language Processing for Low-Resource Senegalese Languages in Social Science Research](https://mlsanity.com/paper/2601.09716/) (2025)
- [MzansiText and MzansiLM: An Open Corpus and Decoder-Only Language Model for South African Languages](https://mlsanity.com/paper/2603.20732/) (2026)
- [DeltaMerge-LowRes: Composing Language and Task Deltas for Low-Resource Adaptation](https://mlsanity.com/paper/2607.13967/) (2026)

## Abstract (arXiv)

African languages have far less in-language content available digitally, making it challenging for question answering systems to satisfy the information needs of users. Cross-lingual open-retrieval question answering (XOR QA) systems -- those that retrieve answer content from other languages while serving people in their native language -- offer a means of filling this gap. To this end, we create AfriQA, the first cross-lingual QA dataset with a focus on African languages. AfriQA includes 12,000+ XOR QA examples across 10 African languages. While previous datasets have focused primarily on languages where cross-lingual QA augments coverage from the target language, AfriQA focuses on languages where cross-lingual answer content is the only high-coverage source of answer content. Because of this, we argue that African languages are one of the most important and realistic use cases for XOR QA. Our experiments demonstrate the poor performance of automatic translation and multilingual retrieval methods. Overall, AfriQA proves challenging for state-of-the-art QA models. We hope that the dataset enables the development of more equitable QA technology.