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We introduce Mintaka, a complex, natural, and multilingual dataset designedfor experimenting with end-to-end question-answering models. Mintaka iscomposed of 20,000 question-answer pairs collected in English, annotated withWikidata entities, and translated into Arabic, French, German, Hindi, Italian,Japanese, Portuguese, and Spanish for a total of 180,000 samples. Mintakaincludes 8 types of complex questions, including superlative, intersection, andmulti-hop questions, which were naturally elicited from crowd workers. We runbaselines over Mintaka, the best of which achieves 38% hits@1 in English and31% hits@1 multilingually, showing that existing models have room forimprovement. We release Mintaka at https://github.com/amazon-research/mintaka.

Mintaka: A Complex, Natural, and Multilingual Dataset for End-to-End Question Answering