Datasets:
Tasks:
Question Answering
Modalities:
Text
Formats:
parquet
Languages:
English
Size:
10K - 100K
ArXiv:
License:
File size: 2,550 Bytes
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{
"default": {
"description": "DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs.\n. DROP is a crowdsourced, adversarially-created, 96k-question benchmark, in which a system must resolve references in a\nquestion, perhaps to multiple input positions, and perform discrete operations over them (such as addition, counting, or\n sorting). These operations require a much more comprehensive understanding of the content of paragraphs than what was\n necessary for prior datasets.\n",
"citation": "@inproceedings{Dua2019DROP,\n author={Dheeru Dua and Yizhong Wang and Pradeep Dasigi and Gabriel Stanovsky and Sameer Singh and Matt Gardner},\n title={DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs},\n booktitle={Proc. of NAACL},\n year={2019}\n}\n",
"homepage": "https://allennlp.org/drop",
"license": "",
"features": {
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},
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}
},
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}
},
"builder_name": "drop",
"dataset_name": "drop",
"config_name": "default",
"version": {
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} |