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Dataset Card for "squadshifts"
Dataset Summary
SquadShifts consists of four new test sets for the Stanford Question Answering Dataset (SQuAD) from four different domains: Wikipedia articles, New York
Times articles, Reddit comments, and Amazon product reviews. Each dataset was generated using the same data generating pipeline, Amazon Mechanical Turk interface, and data cleaning code as the original SQuAD v1.1 dataset. The "new-wikipedia" dataset measures overfitting on the original SQuAD v1.1 dataset. The "new-york-times", "reddit", and "amazon" datasets measure robustness to natural distribution shifts. We encourage SQuAD model developers to also evaluate their methods on these new datasets!
Supported Tasks and Leaderboards
Languages
Dataset Structure
Data Instances
amazon
- Size of downloaded dataset files: 16.50 MB
- Size of the generated dataset: 9.44 MB
- Total amount of disk used: 25.94 MB
An example of 'test' looks as follows.
{
"answers": {
"answer_start": [25],
"text": ["amazon"]
},
"context": "This is a paragraph from amazon.",
"id": "090909",
"question": "Where is this paragraph from?",
"title": "amazon dummy data"
}
new_wiki
- Size of downloaded dataset files: 16.50 MB
- Size of the generated dataset: 7.86 MB
- Total amount of disk used: 24.37 MB
An example of 'test' looks as follows.
{
"answers": {
"answer_start": [25],
"text": ["wikipedia"]
},
"context": "This is a paragraph from wikipedia.",
"id": "090909",
"question": "Where is this paragraph from?",
"title": "new_wiki dummy data"
}
nyt
- Size of downloaded dataset files: 16.50 MB
- Size of the generated dataset: 10.79 MB
- Total amount of disk used: 27.29 MB
An example of 'test' looks as follows.
{
"answers": {
"answer_start": [25],
"text": ["new york times"]
},
"context": "This is a paragraph from new york times.",
"id": "090909",
"question": "Where is this paragraph from?",
"title": "nyt dummy data"
}
- Size of downloaded dataset files: 16.50 MB
- Size of the generated dataset: 9.47 MB
- Total amount of disk used: 25.97 MB
An example of 'test' looks as follows.
{
"answers": {
"answer_start": [25],
"text": ["reddit"]
},
"context": "This is a paragraph from reddit.",
"id": "090909",
"question": "Where is this paragraph from?",
"title": "reddit dummy data"
}
Data Fields
The data fields are the same among all splits.
amazon
id
: astring
feature.title
: astring
feature.context
: astring
feature.question
: astring
feature.answers
: a dictionary feature containing:text
: astring
feature.answer_start
: aint32
feature.
new_wiki
id
: astring
feature.title
: astring
feature.context
: astring
feature.question
: astring
feature.answers
: a dictionary feature containing:text
: astring
feature.answer_start
: aint32
feature.
nyt
id
: astring
feature.title
: astring
feature.context
: astring
feature.question
: astring
feature.answers
: a dictionary feature containing:text
: astring
feature.answer_start
: aint32
feature.
id
: astring
feature.title
: astring
feature.context
: astring
feature.question
: astring
feature.answers
: a dictionary feature containing:text
: astring
feature.answer_start
: aint32
feature.
Data Splits
name | test |
---|---|
amazon | 9885 |
new_wiki | 7938 |
nyt | 10065 |
9803 |
Dataset Creation
Curation Rationale
Source Data
Initial Data Collection and Normalization
Who are the source language producers?
Annotations
Annotation process
Who are the annotators?
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
All the datasets are distributed under the CC BY 4.0 license.
Citation Information
@InProceedings{pmlr-v119-miller20a,
title = {The Effect of Natural Distribution Shift on Question Answering Models},
author = {Miller, John and Krauth, Karl and Recht, Benjamin and Schmidt, Ludwig},
booktitle = {Proceedings of the 37th International Conference on Machine Learning},
pages = {6905--6916},
year = {2020},
editor = {III, Hal Daumé and Singh, Aarti},
volume = {119},
series = {Proceedings of Machine Learning Research},
month = {13--18 Jul},
publisher = {PMLR},
pdf = {http://proceedings.mlr.press/v119/miller20a/miller20a.pdf},
url = {https://proceedings.mlr.press/v119/miller20a.html},
}
Contributions
Thanks to @thomwolf, @lewtun, @millerjohnp, @albertvillanova for adding this dataset.
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