jaredfern/codah
Permissive or share-alike
The declared licence (odc-by) is classified as permissive.
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- Declared licence: odc-by (from the tag)
- Licence files: no licence file at the top level of the repository
- Access gated on Hugging Face: no
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Citation text from the card
@inproceedings{chen-etal-2019-codah,
title = "{CODAH}: An Adversarially-Authored Question Answering Dataset for Common Sense",
author = "Chen, Michael and
D{'}Arcy, Mike and
Liu, Alisa and
Fernandez, Jared and
Downey, Doug",
editor = "Rogers, Anna and
Drozd, Aleksandr and
Rumshisky, Anna and
Goldberg, Yoav",
booktitle = "Proceedings of the 3rd Workshop on Evaluating Vector Space Representations for {NLP}",
month = jun,
year = "2019",
address = "Minneapolis, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W19-2008",
doi = "10.18653/v1/W19-2008",
pages = "63--69",
abstract = "Commonsense reasoning is a critical AI capability, but it is difficult to construct challenging datasets that test common sense. Recent neural question answering systems, based on large pre-trained models of language, have already achieved near-human-level performance on commonsense knowledge benchmarks. These systems do not possess human-level common sense, but are able to exploit limitations of the datasets to achieve human-level scores. We introduce the CODAH dataset, an adversarially-constructed evaluation dataset for testing common sense. CODAH forms a challenging extension to the recently-proposed SWAG dataset, which tests commonsense knowledge using sentence-completion questions that describe situations observed in video. To produce a more difficult dataset,
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