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

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