This repository houses the dataset described in the paper Learning the Difference that Makes a Difference with Counterfactually-Augmented Data. Given documents and their initial labels, we tasked humans to (i) revise each document to accord with a counterfactual target label, subject to producing revisions that (ii) result in internally consistent documents and (iii) avoid any gratuitous changes to facts that are semantically unrelated to the applicability of the label.
| Fields | Description |
|---|---|
| Text | A movie review |
| Sentiment | Positive or Negative Sentiment associated with the movie review |
| Fields | Description |
|---|---|
| sentence1 | The premise sentence |
| sentence2 | The hypothesis sentence |
| gold_label | The truth value of hypothesis given premise |
If you use our data, please cite the paper that introduced the resource with the following BibTeX:
@article{kaushik2020learning,
title={Learning the Difference that Makes a Difference with Counterfactually Augmented Data},
author={Kaushik, Divyansh and Hovy, Eduard and Lipton, Zachary C},
journal={International Conference on Learning Representations (ICLR)},
year={2020}
}
Our follow-up paper explaining the efficacy of CAD was accepted to ICLR 2021. The OOD datasets used in this paper can be found here.
@article{kaushik2021learning,
title={Explaining the Efficacy of Counterfactually Augmented Data},
author={Kaushik, Divyansh and Setlur, Amrith and Hovy, Eduard and Lipton, Zachary C},
journal={International Conference on Learning Representations (ICLR)},
year={2021}
}
We will release the code for the revision platform soon. We are currently cleaning up the codebase to make it easier to use. In the meanwhile, the interface is depicted below:
