Mintaka: A Complex, Natural, and Multilingual Dataset for End-to-End Question Answering
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, Ital...
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| creator | Sen, Priyanka Aji, Alham Fikri Saffari, Amir |
| description | 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. |
| doi_str_mv | 10.48550/arxiv.2210.01613 |
| format | Article |
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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.</description><identifier>DOI: 10.48550/arxiv.2210.01613</identifier><language>eng</language><subject>Computer Science - Computation and Language</subject><creationdate>2022-10</creationdate><rights>http://arxiv.org/licenses/nonexclusive-distrib/1.0</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>228,230,777,882</link.rule.ids><linktorsrc>$$Uhttps://arxiv.org/abs/2210.01613$$EView_record_in_Cornell_University$$FView_record_in_$$GCornell_University$$Hfree_for_read</linktorsrc><backlink>$$Uhttps://doi.org/10.48550/arXiv.2210.01613$$DView paper in arXiv$$Hfree_for_read</backlink></links><search><creatorcontrib>Sen, Priyanka</creatorcontrib><creatorcontrib>Aji, Alham Fikri</creatorcontrib><creatorcontrib>Saffari, Amir</creatorcontrib><title>Mintaka: A Complex, Natural, and Multilingual Dataset for End-to-End Question Answering</title><description>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
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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><doi>10.48550/arxiv.2210.01613</doi><oa>free_for_read</oa></addata></record> |
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| subjects | Computer Science - Computation and Language |
| title | Mintaka: A Complex, Natural, and Multilingual Dataset for End-to-End Question Answering |
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