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    <abstract>We introduce Mintaka, a complex, natural, and multilingual dataset designed for experimenting with end-to-end question-answering models. Mintaka is composed of 20,000 question-answer pairs collected in English, annotated with Wikidata entities, and translated into Arabic, French, German, Hindi, Italian, Japanese, Portuguese, and Spanish for a total of 180,000 samples. Mintaka includes 8 types of complex questions, including superlative, intersection, and multi-hop questions, which were naturally elicited from crowd workers. We run baselines over Mintaka, the best of which achieves 38% hits@1 in English and 31% hits@1 multilingually, showing that existing models have room for improvement. We release Mintaka at https://github.com/amazon-research/mintaka.</abstract>
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%0 Conference Proceedings
%T Mintaka: A Complex, Natural, and Multilingual Dataset for End-to-End Question Answering
%A Sen, Priyanka
%A Aji, Alham Fikri
%A Saffari, Amir
%S Proceedings of the 29th International Conference on Computational Linguistics
%D 2022
%8 October
%I International Committee on Computational Linguistics
%C Gyeongju, Republic of Korea
%F sen-etal-2022-mintaka
%X We introduce Mintaka, a complex, natural, and multilingual dataset designed for experimenting with end-to-end question-answering models. Mintaka is composed of 20,000 question-answer pairs collected in English, annotated with Wikidata entities, and translated into Arabic, French, German, Hindi, Italian, Japanese, Portuguese, and Spanish for a total of 180,000 samples. Mintaka includes 8 types of complex questions, including superlative, intersection, and multi-hop questions, which were naturally elicited from crowd workers. We run baselines over Mintaka, the best of which achieves 38% hits@1 in English and 31% hits@1 multilingually, showing that existing models have room for improvement. We release Mintaka at https://github.com/amazon-research/mintaka.
%U https://aclanthology.org/2022.coling-1.138
%P 1604-1619

Markdown (Informal)

[Mintaka: A Complex, Natural, and Multilingual Dataset for End-to-End Question Answering](https://aclanthology.org/2022.coling-1.138) (Sen et al., COLING 2022)

• Mintaka: A Complex, Natural, and Multilingual Dataset for End-to-End Question Answering (Sen et al., COLING 2022)

ACL

• Priyanka Sen, Alham Fikri Aji, and Amir Saffari. 2022. Mintaka: A Complex, Natural, and Multilingual Dataset for End-to-End Question Answering. In Proceedings of the 29th International Conference on Computational Linguistics, pages 1604–1619, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.