Datasets:
Dataset Card for "drop"
Table of Contents
- Dataset Description
- Dataset Structure
- Dataset Creation
- Considerations for Using the Data
- Additional Information
Dataset Description
- Homepage: https://allennlp.org/drop
- Repository: More Information Needed
- Paper: More Information Needed
- Point of Contact: More Information Needed
- Size of downloaded dataset files: 7.92 MB
- Size of the generated dataset: 105.77 MB
- Total amount of disk used: 113.69 MB
Dataset Summary
DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs. . DROP is a crowdsourced, adversarially-created, 96k-question benchmark, in which a system must resolve references in a question, perhaps to multiple input positions, and perform discrete operations over them (such as addition, counting, or sorting). These operations require a much more comprehensive understanding of the content of paragraphs than what was necessary for prior datasets.
Supported Tasks
Languages
Dataset Structure
We show detailed information for up to 5 configurations of the dataset.
Data Instances
default
- Size of downloaded dataset files: 7.92 MB
- Size of the generated dataset: 105.77 MB
- Total amount of disk used: 113.69 MB
An example of 'validation' looks as follows.
This example was too long and was cropped:
{
"answers_spans": {
"spans": ["Chaz Schilens"]
},
"passage": "\" Hoping to rebound from their loss to the Patriots, the Raiders stayed at home for a Week 16 duel with the Houston Texans. Oak...",
"question": "Who scored the first touchdown of the game?"
}
Data Fields
The data fields are the same among all splits.
default
passage: astringfeature.question: astringfeature.answers_spans: a dictionary feature containing:spans: astringfeature.
Data Splits Sample Size
| name | train | validation |
|---|---|---|
| default | 77409 | 9536 |
Dataset Creation
Curation Rationale
Source Data
Annotations
Personal and Sensitive Information
Considerations for Using the Data
Social Impact of Dataset
Discussion of Biases
Other Known Limitations
Additional Information
Dataset Curators
Licensing Information
Citation Information
@inproceedings{Dua2019DROP,
author={Dheeru Dua and Yizhong Wang and Pradeep Dasigi and Gabriel Stanovsky and Sameer Singh and Matt Gardner},
title={ {DROP}: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs},
booktitle={Proc. of NAACL},
year={2019}
}
Contributions
Thanks to @patrickvonplaten, @thomwolf, @mariamabarham, @lewtun for adding this dataset.