# MKQA: A Linguistically Diverse Benchmark for Multilingual Open Domain Question Answering

Source: [MKQA: A Linguistically Diverse Benchmark for Multilingual Open Domain Question Answering](<https://www.sota2.com/research/paper/68fa6a8c1e209131cea28b31>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T23:27:27.413Z

Authors: Shayne Longpre, Yi Lu, Joachim Daiber

Published: 2020-07-30T03:33:46.000Z

## Abstract

Progress in cross-lingual modeling depends on challenging, realistic, and diverse evaluation sets. We introduce Multilingual Knowledge Questions and Answers (MKQA), an open-domain question answering evaluation set comprising 10k question-answer pairs aligned across 26 typologically diverse languages (260k question-answer pairs in total). Answers are based on a heavily curated, language-independent data representation, making results comparable across languages and independent of language-specific passages. With 26 languages, this dataset supplies the widest range of languages to-date for evaluating question answering. We benchmark a variety of state-of-the-art methods and baselines for generative and extractive question answering, trained on Natural Questions, in zero shot and translation settings. Results indicate this dataset is challenging even in English, but especially in low-resource languages

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2007.15207>)

[PDF](<https://arxiv.org/pdf/2007.15207.pdf>)