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

研究跨语言建模的进展取决于具有挑战性、真实性和多样性的评估集。本文提出了一种开放领域的问题回答评估集 “Multilingual Knowledge Questions and Answers”（MKQA），包括 10k 个问题 - 答案对，跨越 26 种语言（总计 260k 个问题 - 答案对）。结果表明，即使在英语中，这个数据集也具有挑战性，尤其是在低资源语言中。