- Description:
The UnifiedQA benchmark consists of 20 main question answering (QA) datasets (each may have multiple versions) that target different formats as well as various complex linguistic phenomena. These datasets are grouped into several formats/categories, including: extractive QA, abstractive QA, multiple-choice QA, and yes/no QA. Additionally, contrast sets are used for several datasets (denoted with "contrastsets"). These evaluation sets are expert-generated perturbations that deviate from the patterns common in the original dataset. For several datasets that do not come with evidence paragraphs, two variants are included: one where the datasets are used as-is and another that uses paragraphs fetched via an information retrieval system as additional evidence, indicated with "_ir" tags.
More information can be found at: https://github.com/allenai/unifiedqa
Homepage: https://github.com/allenai/unifiedqa
Source code:
tfds.text.unifiedqa.UnifiedQA
Versions:
1.0.0
(default): Initial release.
Feature structure:
FeaturesDict({
'input': string,
'output': string,
})
- Feature documentation:
Feature | Class | Shape | Dtype | Description |
---|---|---|---|---|
FeaturesDict | ||||
input | Tensor | string | ||
output | Tensor | string |
Supervised keys (See
as_supervised
doc):None
Figure (tfds.show_examples): Not supported.
unified_qa/ai2_science_elementary (default config)
Config description: The AI2 Science Questions dataset consists of questions used in student assessments in the United States across elementary and middle school grade levels. Each question is 4-way multiple choice format and may or may not include a diagram element. This set consists of questions used for elementary school grade levels.
Download size:
345.59 KiB
Dataset size:
390.02 KiB
Auto-cached (documentation): Yes
Splits:
Split | Examples |
---|---|
'test' |
542 |
'train' |
623 |
'validation' |
123 |
- Examples (tfds.as_dataframe):
- Citation:
http://data.allenai.org/ai2-science-questions
@inproceedings{khashabi-etal-2020-unifiedqa,
title = "{UNIFIEDQA}: Crossing Format Boundaries with a Single {QA} System",
author = "Khashabi, Daniel and
Min, Sewon and
Khot, Tushar and
Sabharwal, Ashish and
Tafjord, Oyvind and
Clark, Peter and
Hajishirzi, Hannaneh",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.findings-emnlp.171",
doi = "10.18653/v1/2020.findings-emnlp.171",
pages = "1896--1907",
}
Note that each UnifiedQA dataset has its own citation. Please see the source to
see the correct citation for each contained dataset."
unified_qa/ai2_science_middle
Config description: The AI2 Science Questions dataset consists of questions used in student assessments in the United States across elementary and middle school grade levels. Each question is 4-way multiple choice format and may or may not include a diagram element. This set consists of questions used for middle school grade levels.
Download size:
428.41 KiB
Dataset size:
477.40 KiB
Auto-cached (documentation): Yes
Splits:
Split | Examples |
---|---|
'test' |
679 |
'train' |
605 |
'validation' |
125 |
- Examples (tfds.as_dataframe):
- Citation:
http://data.allenai.org/ai2-science-questions
@inproceedings{khashabi-etal-2020-unifiedqa,
title = "{UNIFIEDQA}: Crossing Format Boundaries with a Single {QA} System",
author = "Khashabi, Daniel and
Min, Sewon and
Khot, Tushar and
Sabharwal, Ashish and
Tafjord, Oyvind and
Clark, Peter and
Hajishirzi, Hannaneh",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.findings-emnlp.171",
doi = "10.18653/v1/2020.findings-emnlp.171",
pages = "1896--1907",
}
Note that each UnifiedQA dataset has its own citation. Please see the source to
see the correct citation for each contained dataset."
unified_qa/ambigqa
Config description: AmbigQA is an open-domain question answering task which involves finding every plausible answer, and then rewriting the question for each one to resolve the ambiguity.
Download size:
2.27 MiB
Dataset size:
3.04 MiB
Auto-cached (documentation): Yes
Splits:
Split | Examples |
---|---|
'train' |
19,806 |
'validation' |
5,674 |
- Examples (tfds.as_dataframe):
- Citation:
@inproceedings{min-etal-2020-ambigqa,
title = "{A}mbig{QA}: Answering Ambiguous Open-domain Questions",
author = "Min, Sewon and
Michael, Julian and
Hajishirzi, Hannaneh and
Zettlemoyer, Luke",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.emnlp-main.466",
doi = "10.18653/v1/2020.emnlp-main.466",
pages = "5783--5797",
}
@inproceedings{khashabi-etal-2020-unifiedqa,
title = "{UNIFIEDQA}: Crossing Format Boundaries with a Single {QA} System",
author = "Khashabi, Daniel and
Min, Sewon and
Khot, Tushar and
Sabharwal, Ashish and
Tafjord, Oyvind and
Clark, Peter and
Hajishirzi, Hannaneh",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.findings-emnlp.171",
doi = "10.18653/v1/2020.findings-emnlp.171",
pages = "1896--1907",
}
Note that each UnifiedQA dataset has its own citation. Please see the source to
see the correct citation for each contained dataset."
unified_qa/arc_easy
Config description: This dataset consists of genuine grade-school level, multiple-choice science questions, assembled to encourage research in advanced question-answering. The dataset is partitioned into a Challenge Set and an Easy Set, where the former contains only questions answered incorrectly by both a retrieval-based algorithm and a word co-occurrence algorithm. This set consists of "easy" questions.
Download size:
1.24 MiB
Dataset size:
1.42 MiB
Auto-cached (documentation): Yes
Splits:
Split | Examples |
---|---|
'test' |
2,376 |
'train' |
2,251 |
'validation' |
570 |
- Examples (tfds.as_dataframe):
- Citation:
@article{clark2018think,
title={Think you have solved question answering? try arc, the ai2 reasoning challenge},
author={Clark, Peter and Cowhey, Isaac and Etzioni, Oren and Khot, Tushar and Sabharwal, Ashish and Schoenick, Carissa and Tafjord, Oyvind},
journal={arXiv preprint arXiv:1803.05457},
year={2018}
}
@inproceedings{khashabi-etal-2020-unifiedqa,
title = "{UNIFIEDQA}: Crossing Format Boundaries with a Single {QA} System",
author = "Khashabi, Daniel and
Min, Sewon and
Khot, Tushar and
Sabharwal, Ashish and
Tafjord, Oyvind and
Clark, Peter and
Hajishirzi, Hannaneh",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.findings-emnlp.171",
doi = "10.18653/v1/2020.findings-emnlp.171",
pages = "1896--1907",
}
Note that each UnifiedQA dataset has its own citation. Please see the source to
see the correct citation for each contained dataset."
unified_qa/arc_easy_dev
Config description: This dataset consists of genuine grade-school level, multiple-choice science questions, assembled to encourage research in advanced question-answering. The dataset is partitioned into a Challenge Set and an Easy Set, where the former contains only questions answered incorrectly by both a retrieval-based algorithm and a word co-occurrence algorithm. This set consists of "easy" questions.
Download size:
1.24 MiB
Dataset size:
1.42 MiB
Auto-cached (documentation): Yes
Splits:
Split | Examples |
---|---|
'test' |
2,376 |
'train' |
2,251 |
'validation' |
570 |
- Examples (tfds.as_dataframe):
- Citation:
@article{clark2018think,
title={Think you have solved question answering? try arc, the ai2 reasoning challenge},
author={Clark, Peter and Cowhey, Isaac and Etzioni, Oren and Khot, Tushar and Sabharwal, Ashish and Schoenick, Carissa and Tafjord, Oyvind},
journal={arXiv preprint arXiv:1803.05457},
year={2018}
}
@inproceedings{khashabi-etal-2020-unifiedqa,
title = "{UNIFIEDQA}: Crossing Format Boundaries with a Single {QA} System",
author = "Khashabi, Daniel and
Min, Sewon and
Khot, Tushar and
Sabharwal, Ashish and
Tafjord, Oyvind and
Clark, Peter and
Hajishirzi, Hannaneh",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.findings-emnlp.171",
doi = "10.18653/v1/2020.findings-emnlp.171",
pages = "1896--1907",
}
Note that each UnifiedQA dataset has its own citation. Please see the source to
see the correct citation for each contained dataset."
unified_qa/arc_easy_with_ir
Config description: This dataset consists of genuine grade-school level, multiple-choice science questions, assembled to encourage research in advanced question-answering. The dataset is partitioned into a Challenge Set and an Easy Set, where the former contains only questions answered incorrectly by both a retrieval-based algorithm and a word co-occurrence algorithm. This set consists of "easy" questions. This version includes paragraphs fetched via an information retrieval system as additional evidence.
Download size:
7.00 MiB
Dataset size:
7.17 MiB
Auto-cached (documentation): Yes
Splits:
Split | Examples |
---|---|
'test' |
2,376 |
'train' |
2,251 |
'validation' |
570 |
- Examples (tfds.as_dataframe):
- Citation:
@article{clark2018think,
title={Think you have solved question answering? try arc, the ai2 reasoning challenge},
author={Clark, Peter and Cowhey, Isaac and Etzioni, Oren and Khot, Tushar and Sabharwal, Ashish and Schoenick, Carissa and Tafjord, Oyvind},
journal={arXiv preprint arXiv:1803.05457},
year={2018}
}
@inproceedings{khashabi-etal-2020-unifiedqa,
title = "{UNIFIEDQA}: Crossing Format Boundaries with a Single {QA} System",
author = "Khashabi, Daniel and
Min, Sewon and
Khot, Tushar and
Sabharwal, Ashish and
Tafjord, Oyvind and
Clark, Peter and
Hajishirzi, Hannaneh",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.findings-emnlp.171",
doi = "10.18653/v1/2020.findings-emnlp.171",
pages = "1896--1907",
}
Note that each UnifiedQA dataset has its own citation. Please see the source to
see the correct citation for each contained dataset."
unified_qa/arc_easy_with_ir_dev
Config description: This dataset consists of genuine grade-school level, multiple-choice science questions, assembled to encourage research in advanced question-answering. The dataset is partitioned into a Challenge Set and an Easy Set, where the former contains only questions answered incorrectly by both a retrieval-based algorithm and a word co-occurrence algorithm. This set consists of "easy" questions. This version includes paragraphs fetched via an information retrieval system as additional evidence.
Download size:
7.00 MiB
Dataset size:
7.17 MiB
Auto-cached (documentation): Yes
Splits:
Split | Examples |
---|---|
'test' |
2,376 |
'train' |
2,251 |
'validation' |
570 |
- Examples (tfds.as_dataframe):
- Citation:
@article{clark2018think,
title={Think you have solved question answering? try arc, the ai2 reasoning challenge},
author={Clark, Peter and Cowhey, Isaac and Etzioni, Oren and Khot, Tushar and Sabharwal, Ashish and Schoenick, Carissa and Tafjord, Oyvind},
journal={arXiv preprint arXiv:1803.05457},
year={2018}
}
@inproceedings{khashabi-etal-2020-unifiedqa,
title = "{UNIFIEDQA}: Crossing Format Boundaries with a Single {QA} System",
author = "Khashabi, Daniel and
Min, Sewon and
Khot, Tushar and
Sabharwal, Ashish and
Tafjord, Oyvind and
Clark, Peter and
Hajishirzi, Hannaneh",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.findings-emnlp.171",
doi = "10.18653/v1/2020.findings-emnlp.171",
pages = "1896--1907",
}
Note that each UnifiedQA dataset has its own citation. Please see the source to
see the correct citation for each contained dataset."
unified_qa/arc_hard
Config description: This dataset consists of genuine grade-school level, multiple-choice science questions, assembled to encourage research in advanced question-answering. The dataset is partitioned into a Challenge Set and an Easy Set, where the former contains only questions answered incorrectly by both a retrieval-based algorithm and a word co-occurrence algorithm. This set consists of "hard" questions.
Download size:
758.03 KiB
Dataset size:
848.28 KiB
Auto-cached (documentation): Yes
Splits:
Split | Examples |
---|---|
'test' |
1,172 |
'train' |
1,119 |
'validation' |
299 |
- Examples (tfds.as_dataframe):
- Citation:
@article{clark2018think,
title={Think you have solved question answering? try arc, the ai2 reasoning challenge},
author={Clark, Peter and Cowhey, Isaac and Etzioni, Oren and Khot, Tushar and Sabharwal, Ashish and Schoenick, Carissa and Tafjord, Oyvind},
journal={arXiv preprint arXiv:1803.05457},
year={2018}
}
@inproceedings{khashabi-etal-2020-unifiedqa,
title = "{UNIFIEDQA}: Crossing Format Boundaries with a Single {QA} System",
author = "Khashabi, Daniel and
Min, Sewon and
Khot, Tushar and
Sabharwal, Ashish and
Tafjord, Oyvind and
Clark, Peter and
Hajishirzi, Hannaneh",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.findings-emnlp.171",
doi = "10.18653/v1/2020.findings-emnlp.171",
pages = "1896--1907",
}
Note that each UnifiedQA dataset has its own citation. Please see the source to
see the correct citation for each contained dataset."
unified_qa/arc_hard_dev
Config description: This dataset consists of genuine grade-school level, multiple-choice science questions, assembled to encourage research in advanced question-answering. The dataset is partitioned into a Challenge Set and an Easy Set, where the former contains only questions answered incorrectly by both a retrieval-based algorithm and a word co-occurrence algorithm. This set consists of "hard" questions.
Download size:
758.03 KiB
Dataset size:
848.28 KiB
Auto-cached (documentation): Yes
Splits:
Split | Examples |
---|---|
'test' |
1,172 |
'train' |
1,119 |
'validation' |
299 |
- Examples (tfds.as_dataframe):
- Citation:
@article{clark2018think,
title={Think you have solved question answering? try arc, the ai2 reasoning challenge},
author={Clark, Peter and Cowhey, Isaac and Etzioni, Oren and Khot, Tushar and Sabharwal, Ashish and Schoenick, Carissa and Tafjord, Oyvind},
journal={arXiv preprint arXiv:1803.05457},
year={2018}
}
@inproceedings{khashabi-etal-2020-unifiedqa,
title = "{UNIFIEDQA}: Crossing Format Boundaries with a Single {QA} System",
author = "Khashabi, Daniel and
Min, Sewon and
Khot, Tushar and
Sabharwal, Ashish and
Tafjord, Oyvind and
Clark, Peter and
Hajishirzi, Hannaneh",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.findings-emnlp.171",
doi = "10.18653/v1/2020.findings-emnlp.171",
pages = "1896--1907",
}
Note that each UnifiedQA dataset has its own citation. Please see the source to
see the correct citation for each contained dataset."
unified_qa/arc_hard_with_ir
Config description: This dataset consists of genuine grade-school level, multiple-choice science questions, assembled to encourage research in advanced question-answering. The dataset is partitioned into a Challenge Set and an Easy Set, where the former contains only questions answered incorrectly by both a retrieval-based algorithm and a word co-occurrence algorithm. This set consists of "hard" questions. This version includes paragraphs fetched via an information retrieval system as additional evidence.
Download size:
3.53 MiB
Dataset size:
3.62 MiB
Auto-cached (documentation): Yes
Splits:
Split | Examples |
---|---|
'test' |
1,172 |
'train' |
1,119 |
'validation' |
299 |
- Examples (tfds.as_dataframe):
- Citation:
@article{clark2018think,
title={Think you have solved question answering? try arc, the ai2 reasoning challenge},
author={Clark, Peter and Cowhey, Isaac and Etzioni, Oren and Khot, Tushar and Sabharwal, Ashish and Schoenick, Carissa and Tafjord, Oyvind},
journal={arXiv preprint arXiv:1803.05457},
year={2018}
}
@inproceedings{khashabi-etal-2020-unifiedqa,
title = "{UNIFIEDQA}: Crossing Format Boundaries with a Single {QA} System",
author = "Khashabi, Daniel and
Min, Sewon and
Khot, Tushar and
Sabharwal, Ashish and
Tafjord, Oyvind and
Clark, Peter and
Hajishirzi, Hannaneh",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.findings-emnlp.171",
doi = "10.18653/v1/2020.findings-emnlp.171",
pages = "1896--1907",
}
Note that each UnifiedQA dataset has its own citation. Please see the source to
see the correct citation for each contained dataset."
unified_qa/arc_hard_with_ir_dev
Config description: This dataset consists of genuine grade-school level, multiple-choice science questions, assembled to encourage research in advanced question-answering. The dataset is partitioned into a Challenge Set and an Easy Set, where the former contains only questions answered incorrectly by both a retrieval-based algorithm and a word co-occurrence algorithm. This set consists of "hard" questions. This version includes paragraphs fetched via an information retrieval system as additional evidence.
Download size:
3.53 MiB
Dataset size:
3.62 MiB
Auto-cached (documentation): Yes
Splits:
Split | Examples |
---|---|
'test' |
1,172 |
'train' |
1,119 |
'validation' |
299 |
- Examples (tfds.as_dataframe):
- Citation:
@article{clark2018think,
title={Think you have solved question answering? try arc, the ai2 reasoning challenge},
author={Clark, Peter and Cowhey, Isaac and Etzioni, Oren and Khot, Tushar and Sabharwal, Ashish and Schoenick, Carissa and Tafjord, Oyvind},
journal={arXiv preprint arXiv:1803.05457},
year={2018}
}
@inproceedings{khashabi-etal-2020-unifiedqa,
title = "{UNIFIEDQA}: Crossing Format Boundaries with a Single {QA} System",
author = "Khashabi, Daniel and
Min, Sewon and
Khot, Tushar and
Sabharwal, Ashish and
Tafjord, Oyvind and
Clark, Peter and
Hajishirzi, Hannaneh",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.findings-emnlp.171",
doi = "10.18653/v1/2020.findings-emnlp.171",
pages = "1896--1907",
}
Note that each UnifiedQA dataset has its own citation. Please see the source to
see the correct citation for each contained dataset."
unified_qa/boolq
Config description: BoolQ is a question answering dataset for yes/no questions. These questions are naturally occurring ---they are generated in unprompted and unconstrained settings. Each example is a triplet of (question, passage, answer), with the title of the page as optional additional context. The text-pair classification setup is similar to existing natural language inference tasks.
Download size:
7.77 MiB
Dataset size:
8.20 MiB
Auto-cached (documentation): Yes
Splits:
Split | Examples |
---|---|
'train' |
9,427 |
'validation' |
3,270 |
- Examples (tfds.as_dataframe):
- Citation:
@inproceedings{clark-etal-2019-boolq,
title = "{B}ool{Q}: Exploring the Surprising Difficulty of Natural Yes/No Questions",
author = "Clark, Christopher and
Lee, Kenton and
Chang, Ming-Wei and
Kwiatkowski, Tom and
Collins, Michael and
Toutanova, Kristina",
booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
month = jun,
year = "2019",
address = "Minneapolis, Minnesota",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N19-1300",
doi = "10.18653/v1/N19-1300",
pages = "2924--2936",
}
@inproceedings{khashabi-etal-2020-unifiedqa,
title = "{UNIFIEDQA}: Crossing Format Boundaries with a Single {QA} System",
author = "Khashabi, Daniel and
Min, Sewon and
Khot, Tushar and
Sabharwal, Ashish and
Tafjord, Oyvind and
Clark, Peter and
Hajishirzi, Hannaneh",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.findings-emnlp.171",
doi = "10.18653/v1/2020.findings-emnlp.171",
pages = "1896--1907",
}
Note that each UnifiedQA dataset has its own citation. Please see the source to
see the correct citation for each contained dataset."
unified_qa/boolq_np
Config description: BoolQ is a question answering dataset for yes/no questions. These questions are naturally occurring ---they are generated in unprompted and unconstrained settings. Each example is a triplet of (question, passage, answer), with the title of the page as optional additional context. The text-pair classification setup is similar to existing natural language inference tasks. This version adds natural perturbations to the original version.
Download size:
10.80 MiB
Dataset size:
11.40 MiB
Auto-cached (documentation): Yes
Splits:
Split | Examples |
---|---|
'train' |
9,727 |
'validation' |
7,596 |
- Examples (tfds.as_dataframe):
- Citation:
@inproceedings{khashabi-etal-2020-bang,
title = "More Bang for Your Buck: Natural Perturbation for Robust Question Answering",
author = "Khashabi, Daniel and
Khot, Tushar and
Sabharwal, Ashish",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.emnlp-main.12",
doi = "10.18653/v1/2020.emnlp-main.12",
pages = "163--170",
}
@inproceedings{khashabi-etal-2020-unifiedqa,
title = "{UNIFIEDQA}: Crossing Format Boundaries with a Single {QA} System",
author = "Khashabi, Daniel and
Min, Sewon and
Khot, Tushar and
Sabharwal, Ashish and
Tafjord, Oyvind and
Clark, Peter and
Hajishirzi, Hannaneh",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.findings-emnlp.171",
doi = "10.18653/v1/2020.findings-emnlp.171",
pages = "1896--1907",
}
Note that each UnifiedQA dataset has its own citation. Please see the source to
see the correct citation for each contained dataset."
unified_qa/commonsenseqa
Config description: CommonsenseQA is a new multiple-choice question answering dataset that requires different types of commonsense knowledge to predict the correct answers . It contains questions with one correct answer and four distractor answers.
Download size:
1.79 MiB
Dataset size:
2.19 MiB
Auto-cached (documentation): Yes
Splits:
Split | Examples |
---|---|
'test' |
1,140 |
'train' |
9,741 |
'validation' |
1,221 |
- Examples (tfds.as_dataframe):
- Citation:
@inproceedings{talmor-etal-2019-commonsenseqa,
title = "{C}ommonsense{QA}: A Question Answering Challenge Targeting Commonsense Knowledge",
author = "Talmor, Alon and
Herzig, Jonathan and
Lourie, Nicholas and
Berant, Jonathan",
booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
month = jun,
year = "2019",
address = "Minneapolis, Minnesota",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N19-1421",
doi = "10.18653/v1/N19-1421",
pages = "4149--4158",
}
@inproceedings{khashabi-etal-2020-unifiedqa,
title = "{UNIFIEDQA}: Crossing Format Boundaries with a Single {QA} System",
author = "Khashabi, Daniel and
Min, Sewon and
Khot, Tushar and
Sabharwal, Ashish and
Tafjord, Oyvind and
Clark, Peter and
Hajishirzi, Hannaneh",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.findings-emnlp.171",
doi = "10.18653/v1/2020.findings-emnlp.171",
pages = "1896--1907",
}
Note that each UnifiedQA dataset has its own citation. Please see the source to
see the correct citation for each contained dataset."
unified_qa/commonsenseqa_test
Config description: CommonsenseQA is a new multiple-choice question answering dataset that requires different types of commonsense knowledge to predict the correct answers . It contains questions with one correct answer and four distractor answers.
Download size:
1.79 MiB
Dataset size:
2.19 MiB
Auto-cached (documentation): Yes
Splits:
Split | Examples |
---|---|
'test' |
1,140 |
'train' |
9,741 |
'validation' |
1,221 |
- Examples (tfds.as_dataframe):
- Citation:
@inproceedings{talmor-etal-2019-commonsenseqa,
title = "{C}ommonsense{QA}: A Question Answering Challenge Targeting Commonsense Knowledge",
author = "Talmor, Alon and
Herzig, Jonathan and
Lourie, Nicholas and
Berant, Jonathan",
booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
month = jun,
year = "2019",
address = "Minneapolis, Minnesota",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N19-1421",
doi = "10.18653/v1/N19-1421",
pages = "4149--4158",
}
@inproceedings{khashabi-etal-2020-unifiedqa,
title = "{UNIFIEDQA}: Crossing Format Boundaries with a Single {QA} System",
author = "Khashabi, Daniel and
Min, Sewon and
Khot, Tushar and
Sabharwal, Ashish and
Tafjord, Oyvind and
Clark, Peter and
Hajishirzi, Hannaneh",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.findings-emnlp.171",
doi = "10.18653/v1/2020.findings-emnlp.171",
pages = "1896--1907",
}
Note that each UnifiedQA dataset has its own citation. Please see the source to
see the correct citation for each contained dataset."
unified_qa/contrast_sets_boolq
Config description: BoolQ is a question answering dataset for yes/no questions. These questions are naturally occurring ---they are generated in unprompted and unconstrained settings. Each example is a triplet of (question, passage, answer), with the title of the page as optional additional context. The text-pair classification setup is similar to existing natural language inference tasks. This version uses contrast sets. These evaluation sets are expert-generated perturbations that deviate from the patterns common in the original dataset.
Download size:
438.51 KiB
Dataset size:
462.35 KiB
Auto-cached (documentation): Yes
Splits:
Split | Examples |
---|---|
'train' |
340 |
'validation' |
340 |
- Examples (tfds.as_dataframe):
- Citation:
@inproceedings{clark-etal-2019-boolq,
title = "{B}ool{Q}: Exploring the Surprising Difficulty of Natural Yes/No Questions",
author = "Clark, Christopher and
Lee, Kenton and
Chang, Ming-Wei and
Kwiatkowski, Tom and
Collins, Michael and
Toutanova, Kristina",
booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
month = jun,
year = "2019",
address = "Minneapolis, Minnesota",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N19-1300",
doi = "10.18653/v1/N19-1300",
pages = "2924--2936",
}
@inproceedings{khashabi-etal-2020-unifiedqa,
title = "{UNIFIEDQA}: Crossing Format Boundaries with a Single {QA} System",
author = "Khashabi, Daniel and
Min, Sewon and
Khot, Tushar and
Sabharwal, Ashish and
Tafjord, Oyvind and
Clark, Peter and
Hajishirzi, Hannaneh",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.findings-emnlp.171",
doi = "10.18653/v1/2020.findings-emnlp.171",
pages = "1896--1907",
}
Note that each UnifiedQA dataset has its own citation. Please see the source to
see the correct citation for each contained dataset."
unified_qa/contrast_sets_drop
Config description: DROP is a crowdsourced, adversarially-created QA 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. This version uses contrast sets. These evaluation sets are expert-generated perturbations that deviate from the patterns common in the original dataset.
Download size:
2.20 MiB
Dataset size:
2.26 MiB
Auto-cached (documentation): Yes
Splits:
Split | Examples |
---|---|
'train' |
947 |
'validation' |
947 |
- Examples (tfds.as_dataframe):
- Citation:
@inproceedings{dua-etal-2019-drop,
title = "{DROP}: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs",
author = "Dua, Dheeru and
Wang, Yizhong and
Dasigi, Pradeep and
Stanovsky, Gabriel and
Singh, Sameer and
Gardner, Matt",
booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
month = jun,
year = "2019",
address = "Minneapolis, Minnesota",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N19-1246",
doi = "10.18653/v1/N19-1246",
pages = "2368--2378",
}
@inproceedings{khashabi-etal-2020-unifiedqa,
title = "{UNIFIEDQA}: Crossing Format Boundaries with a Single {QA} System",
author = "Khashabi, Daniel and
Min, Sewon and
Khot, Tushar and
Sabharwal, Ashish and
Tafjord, Oyvind and
Clark, Peter and
Hajishirzi, Hannaneh",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.findings-emnlp.171",
doi = "10.18653/v1/2020.findings-emnlp.171",
pages = "1896--1907",
}
Note that each UnifiedQA dataset has its own citation. Please see the source to
see the correct citation for each contained dataset."
unified_qa/contrast_sets_quoref
Config description: This dataset tests the coreferential reasoning capability of reading comprehension systems. In this span-selection benchmark containing questions over paragraphs from Wikipedia, a system must resolve hard coreferences before selecting the appropriate span(s) in the paragraphs for answering questions. This version uses contrast sets. These evaluation sets are expert-generated perturbations that deviate from the patterns common in the original dataset.
Download size:
2.60 MiB
Dataset size:
2.65 MiB
Auto-cached (documentation): Yes
Splits:
Split | Examples |
---|---|
'train' |
700 |
'validation' |
700 |
- Examples (tfds.as_dataframe):
- Citation:
@inproceedings{dasigi-etal-2019-quoref,
title = "{Q}uoref: A Reading Comprehension Dataset with Questions Requiring Coreferential Reasoning",
author = "Dasigi, Pradeep and
Liu, Nelson F. and
Marasovi{'c}, Ana and
Smith, Noah A. and
Gardner, Matt",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
month = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D19-1606",
doi = "10.18653/v1/D19-1606",
pages = "5925--5932",
}
@inproceedings{khashabi-etal-2020-unifiedqa,
title = "{UNIFIEDQA}: Crossing Format Boundaries with a Single {QA} System",
author = "Khashabi, Daniel and
Min, Sewon and
Khot, Tushar and
Sabharwal, Ashish and
Tafjord, Oyvind and
Clark, Peter and
Hajishirzi, Hannaneh",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.findings-emnlp.171",
doi = "10.18653/v1/2020.findings-emnlp.171",
pages = "1896--1907",
}
Note that each UnifiedQA dataset has its own citation. Please see the source to
see the correct citation for each contained dataset."
unified_qa/contrast_sets_ropes
Config description: This dataset tests a system's ability to apply knowledge from a passage of text to a new situation. A system is presented a background passage containing a causal or qualitative relation(s) (e.g., "animal pollinators increase efficiency of fertilization in flowers"), a novel situation that uses this background, and questions that require reasoning about effects of the relationships in the background passage in the context of the situation. This version uses contrast sets. These evaluation sets are expert-generated perturbations that deviate from the patterns common in the original dataset.
Download size:
1.97 MiB
Dataset size:
2.04 MiB
Auto-cached (documentation): Yes
Splits:
Split | Examples |
---|---|
'train' |
974 |
'validation' |
974 |
- Examples (tfds.as_dataframe):
- Citation:
@inproceedings{lin-etal-2019-reasoning,
title = "Reasoning Over Paragraph Effects in Situations",
author = "Lin, Kevin and
Tafjord, Oyvind and
Clark, Peter and
Gardner, Matt",
booktitle = "Proceedings of the 2nd Workshop on Machine Reading for Question Answering",
month = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D19-5808",
doi = "10.18653/v1/D19-5808",
pages = "58--62",
}
@inproceedings{khashabi-etal-2020-unifiedqa,
title = "{UNIFIEDQA}: Crossing Format Boundaries with a Single {QA} System",
author = "Khashabi, Daniel and
Min, Sewon and
Khot, Tushar and
Sabharwal, Ashish and
Tafjord, Oyvind and
Clark, Peter and
Hajishirzi, Hannaneh",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.findings-emnlp.171",
doi = "10.18653/v1/2020.findings-emnlp.171",
pages = "1896--1907",
}
Note that each UnifiedQA dataset has its own citation. Please see the source to
see the correct citation for each contained dataset."
unified_qa/drop
Config description: DROP is a crowdsourced, adversarially-created QA 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.
Download size:
105.18 MiB
Dataset size:
108.16 MiB
Auto-cached (documentation): Yes
Splits:
Split | Examples |
---|---|
'train' |
77,399 |
'validation' |
9,536 |
- Examples (tfds.as_dataframe):
- Citation:
@inproceedings{dua-etal-2019-drop,
title = "{DROP}: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs",
author = "Dua, Dheeru and
Wang, Yizhong and
Dasigi, Pradeep and
Stanovsky, Gabriel and
Singh, Sameer and
Gardner, Matt",
booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
month = jun,
year = "2019",
address = "Minneapolis, Minnesota",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N19-1246",
doi = "10.18653/v1/N19-1246",
pages = "2368--2378",
}
@inproceedings{khashabi-etal-2020-unifiedqa,
title = "{UNIFIEDQA}: Crossing Format Boundaries with a Single {QA} System",
author = "Khashabi, Daniel and
Min, Sewon and
Khot, Tushar and
Sabharwal, Ashish and
Tafjord, Oyvind and
Clark, Peter and
Hajishirzi, Hannaneh",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.findings-emnlp.171",
doi = "10.18653/v1/2020.findings-emnlp.171",
pages = "1896--1907",
}
Note that each UnifiedQA dataset has its own citation. Please see the source to
see the correct citation for each contained dataset."
unified_qa/mctest
Config description: MCTest requires machines to answer multiple-choice reading comprehension questions about fictional stories, directly tackling the high-level goal of open-domain machine comprehension. Reading comprehension can test advanced abilities such as causal reasoning and understanding the world, yet, by being multiple-choice, still provide a clear metric. By being fictional, the answer typically can be found only in the story itself. The stories and questions are also carefully limited to those a young child would understand, reducing the world knowledge that is required for the task.
Download size:
2.14 MiB
Dataset size:
2.20 MiB
Auto-cached (documentation): Yes
Splits:
Split | Examples |
---|---|
'train' |
1,480 |
'validation' |
320 |
- Examples (tfds.as_dataframe):
- Citation:
@inproceedings{richardson-etal-2013-mctest,
title = "{MCT}est: A Challenge Dataset for the Open-Domain Machine Comprehension of Text",
author = "Richardson, Matthew and
Burges, Christopher J.C. and
Renshaw, Erin",
booktitle = "Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2013",
address = "Seattle, Washington, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D13-1020",
pages = "193--203",
}
@inproceedings{khashabi-etal-2020-unifiedqa,
title = "{UNIFIEDQA}: Crossing Format Boundaries with a Single {QA} System",
author = "Khashabi, Daniel and
Min, Sewon and
Khot, Tushar and
Sabharwal, Ashish and
Tafjord, Oyvind and
Clark, Peter and
Hajishirzi, Hannaneh",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.findings-emnlp.171",
doi = "10.18653/v1/2020.findings-emnlp.171",
pages = "1896--1907",
}
Note that each UnifiedQA dataset has its own citation. Please see the source to
see the correct citation for each contained dataset."
unified_qa/mctest_corrected_the_separator
Config description: MCTest requires machines to answer multiple-choice reading comprehension questions about fictional stories, directly tackling the high-level goal of open-domain machine comprehension. Reading comprehension can test advanced abilities such as causal reasoning and understanding the world, yet, by being multiple-choice, still provide a clear metric. By being fictional, the answer typically can be found only in the story itself. The stories and questions are also carefully limited to those a young child would understand, reducing the world knowledge that is required for the task.
Download size:
2.15 MiB
Dataset size:
2.21 MiB
Auto-cached (documentation): Yes
Splits:
Split | Examples |
---|---|
'train' |
1,480 |
'validation' |
320 |
- Examples (tfds.as_dataframe):
- Citation:
@inproceedings{richardson-etal-2013-mctest,
title = "{MCT}est: A Challenge Dataset for the Open-Domain Machine Comprehension of Text",
author = "Richardson, Matthew and
Burges, Christopher J.C. and
Renshaw, Erin",
booktitle = "Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2013",
address = "Seattle, Washington, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D13-1020",
pages = "193--203",
}
@inproceedings{khashabi-etal-2020-unifiedqa,
title = "{UNIFIEDQA}: Crossing Format Boundaries with a Single {QA} System",
author = "Khashabi, Daniel and
Min, Sewon and
Khot, Tushar and
Sabharwal, Ashish and
Tafjord, Oyvind and
Clark, Peter and
Hajishirzi, Hannaneh",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.findings-emnlp.171",
doi = "10.18653/v1/2020.findings-emnlp.171",
pages = "1896--1907",
}
Note that each UnifiedQA dataset has its own citation. Please see the source to
see the correct citation for each contained dataset."
unified_qa/multirc
Config description: MultiRC is a reading comprehension challenge in which questions can only be answered by taking into account information from multiple sentences. Questions and answers for this challenge were solicited and verified through a 4-step crowdsourcing experiment. The dataset contains questions for paragraphs across 7 different domains ( elementary school science, news, travel guides, fiction stories, etc) bringing in linguistic diversity to the texts and to the questions wordings.
Download size:
897.09 KiB
Dataset size:
918.42 KiB
Auto-cached (documentation): Yes
Splits:
Split | Examples |
---|---|
'train' |
312 |
'validation' |
312 |
- Examples (tfds.as_dataframe):
- Citation:
@inproceedings{khashabi-etal-2018-looking,
title = "Looking Beyond the Surface: A Challenge Set for Reading Comprehension over Multiple Sentences",
author = "Khashabi, Daniel and
Chaturvedi, Snigdha and
Roth, Michael and
Upadhyay, Shyam and
Roth, Dan",
booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
month = jun,
year = "2018",
address = "New Orleans, Louisiana",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N18-1023",
doi = "10.18653/v1/N18-1023",
pages = "252--262",
}
@inproceedings{khashabi-etal-2020-unifiedqa,
title = "{UNIFIEDQA}: Crossing Format Boundaries with a Single {QA} System",
author = "Khashabi, Daniel and
Min, Sewon and
Khot, Tushar and
Sabharwal, Ashish and
Tafjord, Oyvind and
Clark, Peter and
Hajishirzi, Hannaneh",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.findings-emnlp.171",
doi = "10.18653/v1/2020.findings-emnlp.171",
pages = "1896--1907",
}
Note that each UnifiedQA dataset has its own citation. Please see the source to
see the correct citation for each contained dataset."
unified_qa/narrativeqa
Config description: NarrativeQA is an English-lanaguage dataset of stories and corresponding questions designed to test reading comprehension, especially on long documents.
Download size:
308.28 MiB
Dataset size:
311.22 MiB
Auto-cached (documentation): No
Splits:
Split | Examples |
---|---|
'test' |
21,114 |
'train' |
65,494 |
'validation' |
6,922 |
- Examples (tfds.as_dataframe):
- Citation:
@article{kocisky-etal-2018-narrativeqa,
title = "The {N}arrative{QA} Reading Comprehension Challenge",
author = "Ko{
{c} }isk{'y}, Tom{'a}{
{s} } and
Schwarz, Jonathan and
Blunsom, Phil and
Dyer, Chris and
Hermann, Karl Moritz and
Melis, G{'a}bor and
Grefenstette, Edward",
journal = "Transactions of the Association for Computational Linguistics",
volume = "6",
year = "2018",
address = "Cambridge, MA",
publisher = "MIT Press",
url = "https://aclanthology.org/Q18-1023",
doi = "10.1162/tacl_a_00023",
pages = "317--328",
}
@inproceedings{khashabi-etal-2020-unifiedqa,
title = "{UNIFIEDQA}: Crossing Format Boundaries with a Single {QA} System",
author = "Khashabi, Daniel and
Min, Sewon and
Khot, Tushar and
Sabharwal, Ashish and
Tafjord, Oyvind and
Clark, Peter and
Hajishirzi, Hannaneh",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.findings-emnlp.171",
doi = "10.18653/v1/2020.findings-emnlp.171",
pages = "1896--1907",
}
Note that each UnifiedQA dataset has its own citation. Please see the source to
see the correct citation for each contained dataset."
unified_qa/narrativeqa_dev
Config description: NarrativeQA is an English-lanaguage dataset of stories and corresponding questions designed to test reading comprehension, especially on long documents.
Download size:
308.28 MiB
Dataset size:
311.22 MiB
Auto-cached (documentation): No
Splits:
Split | Examples |
---|---|
'test' |
21,114 |
'train' |
65,494 |
'validation' |
6,922 |
- Examples (tfds.as_dataframe):
- Citation:
@article{kocisky-etal-2018-narrativeqa,
title = "The {N}arrative{QA} Reading Comprehension Challenge",
author = "Ko{
{c} }isk{'y}, Tom{'a}{
{s} } and
Schwarz, Jonathan and
Blunsom, Phil and
Dyer, Chris and
Hermann, Karl Moritz and
Melis, G{'a}bor and
Grefenstette, Edward",
journal = "Transactions of the Association for Computational Linguistics",
volume = "6",
year = "2018",
address = "Cambridge, MA",
publisher = "MIT Press",
url = "https://aclanthology.org/Q18-1023",
doi = "10.1162/tacl_a_00023",
pages = "317--328",
}
@inproceedings{khashabi-etal-2020-unifiedqa,
title = "{UNIFIEDQA}: Crossing Format Boundaries with a Single {QA} System",
author = "Khashabi, Daniel and
Min, Sewon and
Khot, Tushar and
Sabharwal, Ashish and
Tafjord, Oyvind and
Clark, Peter and
Hajishirzi, Hannaneh",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.findings-emnlp.171",
doi = "10.18653/v1/2020.findings-emnlp.171",
pages = "1896--1907",
}
Note that each UnifiedQA dataset has its own citation. Please see the source to
see the correct citation for each contained dataset."
unified_qa/natural_questions
Config description: The NQ corpus contains questions from real users, and it requires QA systems to read and comprehend an entire Wikipedia article that may or may not contain the answer to the question. The inclusion of real user questions, and the requirement that solutions should read an entire page to find the answer, cause NQ to be a more realistic and challenging task than prior QA datasets.
Download size:
6.95 MiB
Dataset size:
9.88 MiB
Auto-cached (documentation): Yes
Splits:
Split | Examples |
---|---|
'train' |
96,075 |
'validation' |
2,295 |
- Examples (tfds.as_dataframe):
- Citation:
@article{kwiatkowski-etal-2019-natural,
title = "Natural Questions: A Benchmark for Question Answering Research",
author = "Kwiatkowski, Tom and
Palomaki, Jennimaria and
Redfield, Olivia and
Collins, Michael and
Parikh, Ankur and
Alberti, Chris and
Epstein, Danielle and
Polosukhin, Illia and
Devlin, Jacob and
Lee, Kenton and
Toutanova, Kristina and
Jones, Llion and
Kelcey, Matthew and
Chang, Ming-Wei and
Dai, Andrew M. and
Uszkoreit, Jakob and
Le, Quoc and
Petrov, Slav",
journal = "Transactions of the Association for Computational Linguistics",
volume = "7",
year = "2019",
address = "Cambridge, MA",
publisher = "MIT Press",
url = "https://aclanthology.org/Q19-1026",
doi = "10.1162/tacl_a_00276",
pages = "452--466",
}
@inproceedings{khashabi-etal-2020-unifiedqa,
title = "{UNIFIEDQA}: Crossing Format Boundaries with a Single {QA} System",
author = "Khashabi, Daniel and
Min, Sewon and
Khot, Tushar and
Sabharwal, Ashish and
Tafjord, Oyvind and
Clark, Peter and
Hajishirzi, Hannaneh",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.findings-emnlp.171",
doi = "10.18653/v1/2020.findings-emnlp.171",
pages = "1896--1907",
}
Note that each UnifiedQA dataset has its own citation. Please see the source to
see the correct citation for each contained dataset."
unified_qa/natural_questions_direct_ans
Config description: The NQ corpus contains questions from real users, and it requires QA systems to read and comprehend an entire Wikipedia article that may or may not contain the answer to the question. The inclusion of real user questions, and the requirement that solutions should read an entire page to find the answer, cause NQ to be a more realistic and challenging task than prior QA datasets. This version consists of direct-answer questions.
Download size:
6.82 MiB
Dataset size:
10.19 MiB
Auto-cached (documentation): Yes
Splits:
Split | Examples |
---|---|
'test' |
6,468 |
'train' |
96,676 |
'validation' |
10,693 |
- Examples (tfds.as_dataframe):
- Citation:
@article{kwiatkowski-etal-2019-natural,
title = "Natural Questions: A Benchmark for Question Answering Research",
author = "Kwiatkowski, Tom and
Palomaki, Jennimaria and
Redfield, Olivia and
Collins, Michael and
Parikh, Ankur and
Alberti, Chris and
Epstein, Danielle and
Polosukhin, Illia and
Devlin, Jacob and
Lee, Kenton and
Toutanova, Kristina and
Jones, Llion and
Kelcey, Matthew and
Chang, Ming-Wei and
Dai, Andrew M. and
Uszkoreit, Jakob and
Le, Quoc and
Petrov, Slav",
journal = "Transactions of the Association for Computational Linguistics",
volume = "7",
year = "2019",
address = "Cambridge, MA",
publisher = "MIT Press",
url = "https://aclanthology.org/Q19-1026",
doi = "10.1162/tacl_a_00276",
pages = "452--466",
}
@inproceedings{khashabi-etal-2020-unifiedqa,
title = "{UNIFIEDQA}: Crossing Format Boundaries with a Single {QA} System",
author = "Khashabi, Daniel and
Min, Sewon and
Khot, Tushar and
Sabharwal, Ashish and
Tafjord, Oyvind and
Clark, Peter and
Hajishirzi, Hannaneh",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.findings-emnlp.171",
doi = "10.18653/v1/2020.findings-emnlp.171",
pages = "1896--1907",
}
Note that each UnifiedQA dataset has its own citation. Please see the source to
see the correct citation for each contained dataset."
unified_qa/natural_questions_direct_ans_test
Config description: The NQ corpus contains questions from real users, and it requires QA systems to read and comprehend an entire Wikipedia article that may or may not contain the answer to the question. The inclusion of real user questions, and the requirement that solutions should read an entire page to find the answer, cause NQ to be a more realistic and challenging task than prior QA datasets. This version consists of direct-answer questions.
Download size:
6.82 MiB
Dataset size:
10.19 MiB
Auto-cached (documentation): Yes
Splits:
Split | Examples |
---|---|
'test' |
6,468 |
'train' |
96,676 |
'validation' |
10,693 |
- Examples (tfds.as_dataframe):
- Citation:
@article{kwiatkowski-etal-2019-natural,
title = "Natural Questions: A Benchmark for Question Answering Research",
author = "Kwiatkowski, Tom and
Palomaki, Jennimaria and
Redfield, Olivia and
Collins, Michael and
Parikh, Ankur and
Alberti, Chris and
Epstein, Danielle and
Polosukhin, Illia and
Devlin, Jacob and
Lee, Kenton and
Toutanova, Kristina and
Jones, Llion and
Kelcey, Matthew and
Chang, Ming-Wei and
Dai, Andrew M. and
Uszkoreit, Jakob and
Le, Quoc and
Petrov, Slav",
journal = "Transactions of the Association for Computational Linguistics",
volume = "7",
year = "2019",
address = "Cambridge, MA",
publisher = "MIT Press",
url = "https://aclanthology.org/Q19-1026",
doi = "10.1162/tacl_a_00276",
pages = "452--466",
}
@inproceedings{khashabi-etal-2020-unifiedqa,
title = "{UNIFIEDQA}: Crossing Format Boundaries with a Single {QA} System",
author = "Khashabi, Daniel and
Min, Sewon and
Khot, Tushar and
Sabharwal, Ashish and
Tafjord, Oyvind and
Clark, Peter and
Hajishirzi, Hannaneh",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.findings-emnlp.171",
doi = "10.18653/v1/2020.findings-emnlp.171",
pages = "1896--1907",
}
Note that each UnifiedQA dataset has its own citation. Please see the source to
see the correct citation for each contained dataset."
unified_qa/natural_questions_with_dpr_para
Config description: The NQ corpus contains questions from real users, and it requires QA systems to read and comprehend an entire Wikipedia article that may or may not contain the answer to the question. The inclusion of real user questions, and the requirement that solutions should read an entire page to find the answer, cause NQ to be a more realistic and challenging task than prior QA datasets. This version includes additional paragraphs (obtained using the DPR retrieval engine) to augment each question.
Download size:
319.22 MiB
Dataset size:
322.91 MiB
Auto-cached (documentation): No
Splits:
Split | Examples |
---|---|
'train' |
96,676 |
'validation' |
10,693 |
- Examples (tfds.as_dataframe):
- Citation:
@article{kwiatkowski-etal-2019-natural,
title = "Natural Questions: A Benchmark for Question Answering Research",
author = "Kwiatkowski, Tom and
Palomaki, Jennimaria and
Redfield, Olivia and
Collins, Michael and
Parikh, Ankur and
Alberti, Chris and
Epstein, Danielle and
Polosukhin, Illia and
Devlin, Jacob and
Lee, Kenton and
Toutanova, Kristina and
Jones, Llion and
Kelcey, Matthew and
Chang, Ming-Wei and
Dai, Andrew M. and
Uszkoreit, Jakob and
Le, Quoc and
Petrov, Slav",
journal = "Transactions of the Association for Computational Linguistics",
volume = "7",
year = "2019",
address = "Cambridge, MA",
publisher = "MIT Press",
url = "https://aclanthology.org/Q19-1026",
doi = "10.1162/tacl_a_00276",
pages = "452--466",
}
@inproceedings{khashabi-etal-2020-unifiedqa,
title = "{UNIFIEDQA}: Crossing Format Boundaries with a Single {QA} System",
author = "Khashabi, Daniel and
Min, Sewon and
Khot, Tushar and
Sabharwal, Ashish and
Tafjord, Oyvind and
Clark, Peter and
Hajishirzi, Hannaneh",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.findings-emnlp.171",
doi = "10.18653/v1/2020.findings-emnlp.171",
pages = "1896--1907",
}
Note that each UnifiedQA dataset has its own citation. Please see the source to
see the correct citation for each contained dataset."
unified_qa/natural_questions_with_dpr_para_test
Config description: The NQ corpus contains questions from real users, and it requires QA systems to read and comprehend an entire Wikipedia article that may or may not contain the answer to the question. The inclusion of real user questions, and the requirement that solutions should read an entire page to find the answer, cause NQ to be a more realistic and challenging task than prior QA datasets. This version includes additional paragraphs (obtained using the DPR retrieval engine) to augment each question.
Download size:
306.94 MiB
Dataset size:
310.48 MiB
Auto-cached (documentation): No
Splits:
Split | Examples |
---|---|
'test' |
6,468 |
'train' |
96,676 |
- Examples (tfds.as_dataframe):
- Citation:
@article{kwiatkowski-etal-2019-natural,
title = "Natural Questions: A Benchmark for Question Answering Research",
author = "Kwiatkowski, Tom and
Palomaki, Jennimaria and
Redfield, Olivia and
Collins, Michael and
Parikh, Ankur and
Alberti, Chris and
Epstein, Danielle and
Polosukhin, Illia and
Devlin, Jacob and
Lee, Kenton and
Toutanova, Kristina and
Jones, Llion and
Kelcey, Matthew and
Chang, Ming-Wei and
Dai, Andrew M. and
Uszkoreit, Jakob and
Le, Quoc and
Petrov, Slav",
journal = "Transactions of the Association for Computational Linguistics",
volume = "7",
year = "2019",
address = "Cambridge, MA",
publisher = "MIT Press",
url = "https://aclanthology.org/Q19-1026",
doi = "10.1162/tacl_a_00276",
pages = "452--466",
}
@inproceedings{khashabi-etal-2020-unifiedqa,
title = "{UNIFIEDQA}: Crossing Format Boundaries with a Single {QA} System",
author = "Khashabi, Daniel and
Min, Sewon and
Khot, Tushar and
Sabharwal, Ashish and
Tafjord, Oyvind and
Clark, Peter and
Hajishirzi, Hannaneh",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.findings-emnlp.171",
doi = "10.18653/v1/2020.findings-emnlp.171",
pages = "1896--1907",
}
Note that each UnifiedQA dataset has its own citation. Please see the source to
see the correct citation for each contained dataset."
unified_qa/newsqa
Config description: NewsQA is a challenging machine comprehension dataset of human-generated question-answer pairs. Crowdworkers supply questions and answers based on a set of news articles from CNN, with answers consisting of spans of text from the corresponding articles.
Download size:
283.33 MiB
Dataset size:
285.94 MiB
Auto-cached (documentation): No
Splits:
Split | Examples |
---|---|
'train' |
75,882 |
'validation' |
4,309 |
- Examples (tfds.as_dataframe):
- Citation:
@inproceedings{trischler-etal-2017-newsqa,
title = "{N}ews{QA}: A Machine Comprehension Dataset",
author = "Trischler, Adam and
Wang, Tong and
Yuan, Xingdi and
Harris, Justin and
Sordoni, Alessandro and
Bachman, Philip and
Suleman, Kaheer",
booktitle = "Proceedings of the 2nd Workshop on Representation Learning for {NLP}",
month = aug,
year = "2017",
address = "Vancouver, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W17-2623",
doi = "10.18653/v1/W17-2623",
pages = "191--200",
}
@inproceedings{khashabi-etal-2020-unifiedqa,
title = "{UNIFIEDQA}: Crossing Format Boundaries with a Single {QA} System",
author = "Khashabi, Daniel and
Min, Sewon and
Khot, Tushar and
Sabharwal, Ashish and
Tafjord, Oyvind and
Clark, Peter and
Hajishirzi, Hannaneh",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.findings-emnlp.171",
doi = "10.18653/v1/2020.findings-emnlp.171",
pages = "1896--1907",
}
Note that each UnifiedQA dataset has its own citation. Please see the source to
see the correct citation for each contained dataset."
unified_qa/openbookqa
Config description: OpenBookQA aims to promote research in advanced question-answering, probing a deeper understanding of both the topic (with salient facts summarized as an open book, also provided with the dataset) and the language it is expressed in. In particular, it contains questions that require multi-step reasoning, use of additional common and commonsense knowledge, and rich text comprehension. OpenBookQA is a new kind of question-answering dataset modeled after open book exams for assessing human understanding of a subject.
Download size:
942.34 KiB
Dataset size:
1.11 MiB
Auto-cached (documentation): Yes
Splits:
Split | Examples |
---|---|
'test' |
500 |
'train' |
4,957 |
'validation' |
500 |
- Examples (tfds.as_dataframe):
- Citation:
@inproceedings{mihaylov-etal-2018-suit,
title = "Can a Suit of Armor Conduct Electricity? A New Dataset for Open Book Question Answering",
author = "Mihaylov, Todor and
Clark, Peter and
Khot, Tushar and
Sabharwal, Ashish",
booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
month = oct # "-" # nov,
year = "2018",
address = "Brussels, Belgium",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D18-1260",
doi = "10.18653/v1/D18-1260",
pages = "2381--2391",
}
@inproceedings{khashabi-etal-2020-unifiedqa,
title = "{UNIFIEDQA}: Crossing Format Boundaries with a Single {QA} System",
author = "Khashabi, Daniel and
Min, Sewon and
Khot, Tushar and
Sabharwal, Ashish and
Tafjord, Oyvind and
Clark, Peter and
Hajishirzi, Hannaneh",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.findings-emnlp.171",
doi = "10.18653/v1/2020.findings-emnlp.171",
pages = "1896--1907",
}
Note that each UnifiedQA dataset has its own citation. Please see the source to
see the correct citation for each contained dataset."
unified_qa/openbookqa_dev
Config description: OpenBookQA aims to promote research in advanced question-answering, probing a deeper understanding of both the topic (with salient facts summarized as an open book, also provided with the dataset) and the language it is expressed in. In particular, it contains questions that require multi-step reasoning, use of additional common and commonsense knowledge, and rich text comprehension. OpenBookQA is a new kind of question-answering dataset modeled after open book exams for assessing human understanding of a subject.
Download size:
942.34 KiB
Dataset size:
1.11 MiB
Auto-cached (documentation): Yes
Splits:
Split | Examples |
---|---|
'test' |
500 |
'train' |
4,957 |
'validation' |
500 |
- Examples (tfds.as_dataframe):
- Citation:
@inproceedings{mihaylov-etal-2018-suit,
title = "Can a Suit of Armor Conduct Electricity? A New Dataset for Open Book Question Answering",
author = "Mihaylov, Todor and
Clark, Peter and
Khot, Tushar and
Sabharwal, Ashish",
booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
month = oct # "-" # nov,
year = "2018",
address = "Brussels, Belgium",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D18-1260",
doi = "10.18653/v1/D18-1260",
pages = "2381--2391",
}
@inproceedings{khashabi-etal-2020-unifiedqa,
title = "{UNIFIEDQA}: Crossing Format Boundaries with a Single {QA} System",
author = "Khashabi, Daniel and
Min, Sewon and
Khot, Tushar and
Sabharwal, Ashish and
Tafjord, Oyvind and
Clark, Peter and
Hajishirzi, Hannaneh",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.findings-emnlp.171",
doi = "10.18653/v1/2020.findings-emnlp.171",
pages = "1896--1907",
}
Note that each UnifiedQA dataset has its own citation. Please see the source to
see the correct citation for each contained dataset."
unified_qa/openbookqa_with_ir
Config description: OpenBookQA aims to promote research in advanced question-answering, probing a deeper understanding of both the topic (with salient facts summarized as an open book, also provided with the dataset) and the language it is expressed in. In particular, it contains questions that require multi-step reasoning, use of additional common and commonsense knowledge, and rich text comprehension. OpenBookQA is a new kind of question-answering dataset modeled after open book exams for assessing human understanding of a subject. This version includes paragraphs fetched via an information retrieval system as additional evidence.
Download size:
6.08 MiB
Dataset size:
6.28 MiB
Auto-cached (documentation): Yes
Splits:
Split | Examples |
---|---|
'test' |
500 |
'train' |
4,957 |
'validation' |
500 |
- Examples (tfds.as_dataframe):
- Citation:
@inproceedings{mihaylov-etal-2018-suit,
title = "Can a Suit of Armor Conduct Electricity? A New Dataset for Open Book Question Answering",
author = "Mihaylov, Todor and
Clark, Peter and
Khot, Tushar and
Sabharwal, Ashish",
booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
month = oct # "-" # nov,
year = "2018",
address = "Brussels, Belgium",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D18-1260",
doi = "10.18653/v1/D18-1260",
pages = "2381--2391",
}
@inproceedings{khashabi-etal-2020-unifiedqa,
title = "{UNIFIEDQA}: Crossing Format Boundaries with a Single {QA} System",
author = "Khashabi, Daniel and
Min, Sewon and
Khot, Tushar and
Sabharwal, Ashish and
Tafjord, Oyvind and
Clark, Peter and
Hajishirzi, Hannaneh",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.findings-emnlp.171",
doi = "10.18653/v1/2020.findings-emnlp.171",
pages = "1896--1907",
}
Note that each UnifiedQA dataset has its own citation. Please see the source to
see the correct citation for each contained dataset."
unified_qa/openbookqa_with_ir_dev
Config description: OpenBookQA aims to promote research in advanced question-answering, probing a deeper understanding of both the topic (with salient facts summarized as an open book, also provided with the dataset) and the language it is expressed in. In particular, it contains questions that require multi-step reasoning, use of additional common and commonsense knowledge, and rich text comprehension. OpenBookQA is a new kind of question-answering dataset modeled after open book exams for assessing human understanding of a subject. This version includes paragraphs fetched via an information retrieval system as additional evidence.
Download size:
6.08 MiB
Dataset size:
6.28 MiB
Auto-cached (documentation): Yes
Splits:
Split | Examples |
---|---|
'test' |
500 |
'train' |
4,957 |
'validation' |
500 |
- Examples (tfds.as_dataframe):
- Citation:
@inproceedings{mihaylov-etal-2018-suit,
title = "Can a Suit of Armor Conduct Electricity? A New Dataset for Open Book Question Answering",
author = "Mihaylov, Todor and
Clark, Peter and
Khot, Tushar and
Sabharwal, Ashish",
booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
month = oct # "-" # nov,
year = "2018",
address = "Brussels, Belgium",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D18-1260",
doi = "10.18653/v1/D18-1260",
pages = "2381--2391",
}
@inproceedings{khashabi-etal-2020-unifiedqa,
title = "{UNIFIEDQA}: Crossing Format Boundaries with a Single {QA} System",
author = "Khashabi, Daniel and
Min, Sewon and
Khot, Tushar and
Sabharwal, Ashish and
Tafjord, Oyvind and
Clark, Peter and
Hajishirzi, Hannaneh",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.findings-emnlp.171",
doi = "10.18653/v1/2020.findings-emnlp.171",
pages = "1896--1907",
}
Note that each UnifiedQA dataset has its own citation. Please see the source to
see the correct citation for each contained dataset."
unified_qa/physical_iqa
Config description: This is a dataset for benchmarking progress in physical commonsense understanding. The underlying task is multiple choice question answering: given a question q and two possible solutions s1, s2, a model or a human must choose the most appropriate solution, of which exactly one is correct. The dataset focuses on everyday situations with a preference for atypical solutions. The dataset is inspired by instructables.com, which provides users with instructions on how to build, craft, bake, or manipulate objects using everyday materials. Annotators are asked to provide semantic perturbations or alternative approaches which are otherwise syntactically and topically similar to ensure physical knowledge is targeted. The dataset is further cleaned of basic artifacts using the AFLite algorithm.
Download size:
6.01 MiB
Dataset size:
6.59 MiB
Auto-cached (documentation): Yes
Splits:
Split | Examples |
---|---|
'train' |
16,113 |
'validation' |
1,838 |
- Examples (tfds.as_dataframe):
- Citation:
@inproceedings{bisk2020piqa,
title={Piqa: Reasoning about physical commonsense in natural language},
author={Bisk, Yonatan and Zellers, Rowan and Gao, Jianfeng and Choi, Yejin and others},
booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
volume={34},
number={05},
pages={7432--7439},
year={2020}
}
@inproceedings{khashabi-etal-2020-unifiedqa,
title = "{UNIFIEDQA}: Crossing Format Boundaries with a Single {QA} System",
author = "Khashabi, Daniel and
Min, Sewon and
Khot, Tushar and
Sabharwal, Ashish and
Tafjord, Oyvind and
Clark, Peter and
Hajishirzi, Hannaneh",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.findings-emnlp.171",
doi = "10.18653/v1/2020.findings-emnlp.171",
pages = "1896--1907",
}
Note that each UnifiedQA dataset has its own citation. Please see the source to
see the correct citation for each contained dataset."
unified_qa/qasc
Config description: QASC is a question-answering dataset with a focus on sentence composition. It consists of 8-way multiple-choice questions about grade school science, and comes with a corpus of 17M sentences.
Download size:
1.75 MiB
Dataset size:
2.09 MiB
Auto-cached (documentation): Yes
Splits:
Split | Examples |
---|---|
'test' |
920 |
'train' |
8,134 |
'validation' |
926 |
- Examples (tfds.as_dataframe):
- Citation:
@inproceedings{khot2020qasc,
title={Qasc: A dataset for question answering via sentence composition},
author={Khot, Tushar and Clark, Peter and Guerquin, Michal and Jansen, Peter and Sabharwal, Ashish},
booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
volume={34},
number={05},
pages={8082--8090},
year={2020}
}
@inproceedings{khashabi-etal-2020-unifiedqa,
title = "{UNIFIEDQA}: Crossing Format Boundaries with a Single {QA} System",
author = "Khashabi, Daniel and
Min, Sewon and
Khot, Tushar and
Sabharwal, Ashish and
Tafjord, Oyvind and
Clark, Peter and
Hajishirzi, Hannaneh",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.findings-emnlp.171",
doi = "10.18653/v1/2020.findings-emnlp.171",
pages = "1896--1907",
}
Note that each UnifiedQA dataset has its own citation. Please see the source to
see the correct citation for each contained dataset."
unified_qa/qasc_test
Config description: QASC is a question-answering dataset with a focus on sentence composition. It consists of 8-way multiple-choice questions about grade school science, and comes with a corpus of 17M sentences.
Download size:
1.75 MiB
Dataset size:
2.09 MiB
Auto-cached (documentation): Yes
Splits:
Split | Examples |
---|---|
'test' |
920 |
'train' |
8,134 |
'validation' |
926 |
- Examples (tfds.as_dataframe):
- Citation:
@inproceedings{khot2020qasc,
title={Qasc: A dataset for question answering via sentence composition},
author={Khot, Tushar and Clark, Peter and Guerquin, Michal and Jansen, Peter and Sabharwal, Ashish},
booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
volume={34},
number={05},
pages={8082--8090},
year={2020}
}
@inproceedings{khashabi-etal-2020-unifiedqa,
title = "{UNIFIEDQA}: Crossing Format Boundaries with a Single {QA} System",
author = "Khashabi, Daniel and
Min, Sewon and
Khot, Tushar and
Sabharwal, Ashish and
Tafjord, Oyvind and
Clark, Peter and
Hajishirzi, Hannaneh",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.findings-emnlp.171",
doi = "10.18653/v1/2020.findings-emnlp.171",
pages = "1896--1907",
}
Note that each UnifiedQA dataset has its own citation. Please see the source to
see the correct citation for each contained dataset."
unified_qa/qasc_with_ir
Config description: QASC is a question-answering dataset with a focus on sentence composition. It consists of 8-way multiple-choice questions about grade school science, and comes with a corpus of 17M sentences. This version includes paragraphs fetched via an information retrieval system as additional evidence.
Download size:
16.95 MiB
Dataset size:
17.30 MiB
Auto-cached (documentation): Yes
Splits:
Split | Examples |
---|---|
'test' |
920 |
'train' |
8,134 |
'validation' |
926 |
- Examples (tfds.as_dataframe):
- Citation:
@inproceedings{khot2020qasc,
title={Qasc: A dataset for question answering via sentence composition},
author={Khot, Tushar and Clark, Peter and Guerquin, Michal and Jansen, Peter and Sabharwal, Ashish},
booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
volume={34},
number={05},
pages={8082--8090},
year={2020}
}
@inproceedings{khashabi-etal-2020-unifiedqa,
title = "{UNIFIEDQA}: Crossing Format Boundaries with a Single {QA} System",
author = "Khashabi, Daniel and
Min, Sewon and
Khot, Tushar and
Sabharwal, Ashish and
Tafjord, Oyvind and
Clark, Peter and
Hajishirzi, Hannaneh",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.findings-emnlp.171",
doi = "10.18653/v1/2020.findings-emnlp.171",
pages = "1896--1907",
}
Note that each UnifiedQA dataset has its own citation. Please see the source to
see the correct citation for each contained dataset."
unified_qa/qasc_with_ir_test
Config description: QASC is a question-answering dataset with a focus on sentence composition. It consists of 8-way multiple-choice questions about grade school science, and comes with a corpus of 17M sentences. This version includes paragraphs fetched via an information retrieval system as additional evidence.
Download size:
16.95 MiB
Dataset size:
17.30 MiB
Auto-cached (documentation): Yes
Splits:
Split | Examples |
---|---|
'test' |
920 |
'train' |
8,134 |
'validation' |
926 |
- Examples (tfds.as_dataframe):
- Citation:
@inproceedings{khot2020qasc,
title={Qasc: A dataset for question answering via sentence composition},
author={Khot, Tushar and Clark, Peter and Guerquin, Michal and Jansen, Peter and Sabharwal, Ashish},
booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
volume={34},
number={05},
pages={8082--8090},
year={2020}
}
@inproceedings{khashabi-etal-2020-unifiedqa,
title = "{UNIFIEDQA}: Crossing Format Boundaries with a Single {QA} System",
author = "Khashabi, Daniel and
Min, Sewon and
Khot, Tushar and
Sabharwal, Ashish and
Tafjord, Oyvind and
Clark, Peter and
Hajishirzi, Hannaneh",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.findings-emnlp.171",
doi = "10.18653/v1/2020.findings-emnlp.171",
pages = "1896--1907",
}
Note that each UnifiedQA dataset has its own citation. Please see the source to
see the correct citation for each contained dataset."
unified_qa/quoref
Config description: This dataset tests the coreferential reasoning capability of reading comprehension systems. In this span-selection benchmark containing questions over paragraphs from Wikipedia, a system must resolve hard coreferences before selecting the appropriate span(s) in the paragraphs for answering questions.
Download size:
51.43 MiB
Dataset size:
52.29 MiB
Auto-cached (documentation): Yes
Splits:
Split | Examples |
---|---|
'train' |
22,265 |
'validation' |
2,768 |
- Examples (tfds.as_dataframe):
- Citation:
@inproceedings{dasigi-etal-2019-quoref,
title = "{Q}uoref: A Reading Comprehension Dataset with Questions Requiring Coreferential Reasoning",
author = "Dasigi, Pradeep and
Liu, Nelson F. and
Marasovi{'c}, Ana and
Smith, Noah A. and
Gardner, Matt",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
month = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D19-1606",
doi = "10.18653/v1/D19-1606",
pages = "5925--5932",
}
@inproceedings{khashabi-etal-2020-unifiedqa,
title = "{UNIFIEDQA}: Crossing Format Boundaries with a Single {QA} System",
author = "Khashabi, Daniel and
Min, Sewon and
Khot, Tushar and
Sabharwal, Ashish and
Tafjord, Oyvind and
Clark, Peter and
Hajishirzi, Hannaneh",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.findings-emnlp.171",
doi = "10.18653/v1/2020.findings-emnlp.171",
pages = "1896--1907",
}
Note that each UnifiedQA dataset has its own citation. Please see the source to
see the correct citation for each contained dataset."
unified_qa/race_string
Config description: Race is a large-scale reading comprehension dataset. The dataset is collected from English examinations in China, which are designed for middle school and high school students. The dataset can be served as the training and test sets for machine comprehension.
Download size:
167.97 MiB
Dataset size:
171.23 MiB
Auto-cached (documentation): Yes (test, validation), Only when
shuffle_files=False
(train)Splits:
Split | Examples |
---|---|
'test' |
4,934 |
'train' |
87,863 |
'validation' |
4,887 |
- Examples (tfds.as_dataframe):
- Citation:
@inproceedings{lai-etal-2017-race,
title = "{RACE}: Large-scale {R}e{A}ding Comprehension Dataset From Examinations",
author = "Lai, Guokun and
Xie, Qizhe and
Liu, Hanxiao and
Yang, Yiming and
Hovy, Eduard",
booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
month = sep,
year = "2017",
address = "Copenhagen, Denmark",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D17-1082",
doi = "10.18653/v1/D17-1082",
pages = "785--794",
}
@inproceedings{khashabi-etal-2020-unifiedqa,
title = "{UNIFIEDQA}: Crossing Format Boundaries with a Single {QA} System",
author = "Khashabi, Daniel and
Min, Sewon and
Khot, Tushar and
Sabharwal, Ashish and
Tafjord, Oyvind and
Clark, Peter and
Hajishirzi, Hannaneh",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.findings-emnlp.171",
doi = "10.18653/v1/2020.findings-emnlp.171",
pages = "1896--1907",
}
Note that each UnifiedQA dataset has its own citation. Please see the source to
see the correct citation for each contained dataset."
unified_qa/race_string_dev
Config description: Race is a large-scale reading comprehension dataset. The dataset is collected from English examinations in China, which are designed for middle school and high school students. The dataset can be served as the training and test sets for machine comprehension.
Download size:
167.97 MiB
Dataset size:
171.23 MiB
Auto-cached (documentation): Yes (test, validation), Only when
shuffle_files=False
(train)Splits:
Split | Examples |
---|---|
'test' |
4,934 |
'train' |
87,863 |
'validation' |
4,887 |
- Examples (tfds.as_dataframe):
- Citation:
@inproceedings{lai-etal-2017-race,
title = "{RACE}: Large-scale {R}e{A}ding Comprehension Dataset From Examinations",
author = "Lai, Guokun and
Xie, Qizhe and
Liu, Hanxiao and
Yang, Yiming and
Hovy, Eduard",
booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
month = sep,
year = "2017",
address = "Copenhagen, Denmark",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D17-1082",
doi = "10.18653/v1/D17-1082",
pages = "785--794",
}
@inproceedings{khashabi-etal-2020-unifiedqa,
title = "{UNIFIEDQA}: Crossing Format Boundaries with a Single {QA} System",
author = "Khashabi, Daniel and
Min, Sewon and
Khot, Tushar and
Sabharwal, Ashish and
Tafjord, Oyvind and
Clark, Peter and
Hajishirzi, Hannaneh",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.findings-emnlp.171",
doi = "10.18653/v1/2020.findings-emnlp.171",
pages = "1896--1907",
}
Note that each UnifiedQA dataset has its own citation. Please see the source to
see the correct citation for each contained dataset."
unified_qa/ropes
Config description: This dataset tests a system's ability to apply knowledge from a passage of text to a new situation. A system is presented a background passage containing a causal or qualitative relation(s) (e.g., "animal pollinators increase efficiency of fertilization in flowers"), a novel situation that uses this background, and questions that require reasoning about effects of the relationships in the background passage in the context of the situation.
Download size:
12.91 MiB
Dataset size:
13.35 MiB
Auto-cached (documentation): Yes
Splits:
Split | Examples |
---|---|
'train' |
10,924 |
'validation' |
1,688 |
- Examples (tfds.as_dataframe):
- Citation:
@inproceedings{lin-etal-2019-reasoning,
title = "Reasoning Over Paragraph Effects in Situations",
author = "Lin, Kevin and
Tafjord, Oyvind and
Clark, Peter and
Gardner, Matt",
booktitle = "Proceedings of the 2nd Workshop on Machine Reading for Question Answering",
month = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D19-5808",
doi = "10.18653/v1/D19-5808",
pages = "58--62",
}
@inproceedings{khashabi-etal-2020-unifiedqa,
title = "{UNIFIEDQA}: Crossing Format Boundaries with a Single {QA} System",
author = "Khashabi, Daniel and
Min, Sewon and
Khot, Tushar and
Sabharwal, Ashish and
Tafjord, Oyvind and
Clark, Peter and
Hajishirzi, Hannaneh",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.findings-emnlp.171",
doi = "10.18653/v1/2020.findings-emnlp.171",
pages = "1896--1907",
}
Note that each UnifiedQA dataset has its own citation. Please see the source to
see the correct citation for each contained dataset."
unified_qa/social_iqa
Config description: This is a large-scale benchmark for commonsense reasoning about social situations. Social IQa contains multiple choice questions for probing emotional and social intelligence in a variety of everyday situations. Through crowdsourcing, commonsense questions along with correct and incorrect answers about social interactions are collected, using a new framework that mitigates stylistic artifacts in incorrect answers by asking workers to provide the right answer to a different but related question.
Download size:
7.08 MiB
Dataset size:
8.22 MiB
Auto-cached (documentation): Yes
Splits:
Split | Examples |
---|---|
'train' |
33,410 |
'validation' |
1,954 |
- Examples (tfds.as_dataframe):
- Citation:
@inproceedings{sap-etal-2019-social,
title = "Social {IQ}a: Commonsense Reasoning about Social Interactions",
author = "Sap, Maarten and
Rashkin, Hannah and
Chen, Derek and
Le Bras, Ronan and
Choi, Yejin",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
month = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D19-1454",
doi = "10.18653/v1/D19-1454",
pages = "4463--4473",
}
@inproceedings{khashabi-etal-2020-unifiedqa,
title = "{UNIFIEDQA}: Crossing Format Boundaries with a Single {QA} System",
author = "Khashabi, Daniel and
Min, Sewon and
Khot, Tushar and
Sabharwal, Ashish and
Tafjord, Oyvind and
Clark, Peter and
Hajishirzi, Hannaneh",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.findings-emnlp.171",
doi = "10.18653/v1/2020.findings-emnlp.171",
pages = "1896--1907",
}
Note that each UnifiedQA dataset has its own citation. Please see the source to
see the correct citation for each contained dataset."
unified_qa/squad1_1
Config description: This is a reading comprehension dataset consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to each question is a segment of text from the corresponding reading passage.
Download size:
80.62 MiB
Dataset size:
83.99 MiB
Auto-cached (documentation): Yes
Splits:
Split | Examples |
---|---|
'train' |
87,514 |
'validation' |
10,570 |
- Examples (tfds.as_dataframe):
- Citation:
@inproceedings{rajpurkar-etal-2016-squad,
title = "{SQ}u{AD}: 100,000+ Questions for Machine Comprehension of Text",
author = "Rajpurkar, Pranav and
Zhang, Jian and
Lopyrev, Konstantin and
Liang, Percy",
booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2016",
address = "Austin, Texas",
publisher