CODAH (COmmonsense Dataset Adversarially-authored by Humans)
Introduced by Chen et al. in CODAH: An Adversarially-Authored Question Answering Dataset for Common Sense
The COmmonsense Dataset Adversarially-authored by Humans (CODAH) is an evaluation set for commonsense question-answering in the sentence completion style of SWAG. As opposed to other automatically generated NLI datasets, CODAH is adversarially constructed by humans who can view feedback from a pre-trained model and use this information to design challenging commonsense questions. It contains 2801 questions in total, and uses 5-fold cross validation for evaluation.
Source: CODAH DatasetBenchmarks
| Trend | Task | Dataset Variant | Best Model | Paper | Code |
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CODAH
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G-DAUG-Combo + RoBERTa-Large
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CODAH
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BERT Large
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Papers
| Paper | Code | Results | Date | Stars |
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