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Dataset Card for "search_qa"
Dataset Summary
We publicly release a new large-scale dataset, called SearchQA, for machine comprehension, or question-answering. Unlike recently released datasets, such as DeepMind CNN/DailyMail and SQuAD, the proposed SearchQA was constructed to reflect a full pipeline of general question-answering. That is, we start not from an existing article and generate a question-answer pair, but start from an existing question-answer pair, crawled from J! Archive, and augment it with text snippets retrieved by Google. Following this approach, we built SearchQA, which consists of more than 140k question-answer pairs with each pair having 49.6 snippets on average. Each question-answer-context tuple of the SearchQA comes with additional meta-data such as the snippet's URL, which we believe will be valuable resources for future research. We conduct human evaluation as well as test two baseline methods, one simple word selection and the other deep learning based, on the SearchQA. We show that there is a meaningful gap between the human and machine performances. This suggests that the proposed dataset could well serve as a benchmark for question-answering.
Supported Tasks and Leaderboards
Languages
Dataset Structure
Data Instances
raw_jeopardy
- Size of downloaded dataset files: 3.31 GB
- Size of the generated dataset: 7.77 GB
- Total amount of disk used: 11.09 GB
An example of 'train' looks as follows.
train_test_val
- Size of downloaded dataset files: 3.15 GB
- Size of the generated dataset: 7.51 GB
- Total amount of disk used: 10.66 GB
An example of 'validation' looks as follows.
Data Fields
The data fields are the same among all splits.
raw_jeopardy
category
: astring
feature.air_date
: astring
feature.question
: astring
feature.value
: astring
feature.answer
: astring
feature.round
: astring
feature.show_number
: aint32
feature.search_results
: a dictionary feature containing:urls
: astring
feature.snippets
: astring
feature.titles
: astring
feature.related_links
: astring
feature.
train_test_val
category
: astring
feature.air_date
: astring
feature.question
: astring
feature.value
: astring
feature.answer
: astring
feature.round
: astring
feature.show_number
: aint32
feature.search_results
: a dictionary feature containing:urls
: astring
feature.snippets
: astring
feature.titles
: astring
feature.related_links
: astring
feature.
Data Splits
raw_jeopardy
train | |
---|---|
raw_jeopardy | 216757 |
train_test_val
train | validation | test | |
---|---|---|---|
train_test_val | 151295 | 21613 | 43228 |
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
Citation Information
@article{DBLP:journals/corr/DunnSHGCC17,
author = {Matthew Dunn and
Levent Sagun and
Mike Higgins and
V. Ugur G{"{u}}ney and
Volkan Cirik and
Kyunghyun Cho},
title = {SearchQA: {A} New Q{\&}A Dataset Augmented with Context from a
Search Engine},
journal = {CoRR},
volume = {abs/1704.05179},
year = {2017},
url = {http://arxiv.org/abs/1704.05179},
archivePrefix = {arXiv},
eprint = {1704.05179},
timestamp = {Mon, 13 Aug 2018 16:47:09 +0200},
biburl = {https://dblp.org/rec/journals/corr/DunnSHGCC17.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
Contributions
Thanks to @lewtun, @mariamabarham, @lhoestq, @thomwolf for adding this dataset.
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