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 Dataset


Paper Code Results Date Stars


Similar Datasets


  • Unknown