---
task_categories:
- text-classification
- text-generation
language:
- en
pretty_name: The Appropriateness Corpus Extension
size_categories:
- 10K<n<100K
---
# The Appropriateness Corpus Extension

<!-- Provide a quick summary of the dataset. -->

**The Appropriateness Corpus Extension** is a collection of ~50k arguments automatically annotated for (in)appropriateness based on an ensemble of the classifiers created as part of the [Modeling Appropriate Language in Argumentation](https://aclanthology.org/2023.acl-long.238/) paper published at the ACL2023.

You can find the original dataset which was manually annotated [here](https://huggingface.co/datasets/timonziegenbein/appropriateness-corpus/tree/main).

### Dataset Sources

<!-- Provide the basic links for the dataset. -->

- **Repository:** [https://github.com/timonziegenbein/inappropriateness-mitigation]
- **Paper [optional]:** [LLM-based Rewriting of Inappropriate Argumentation using Reinforcement Learning from Machine Feedback
](https://arxiv.org/abs/2406.03363)


## Dataset Structure

<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->

The dataset columns present the argument, the issue the argument was written for, and the inappropriateness score; if the value in a column is above 0.5, the argument would be considered inappropriate. Otherwise, it would be considered to be appropriate.

## Citation

<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->

If you are interested in using the corpus, please cite the following papers:
[Modeling Appropriate Language in Argumentation](https://aclanthology.org/2023.acl-long.238) (Ziegenbein et al., ACL 2023)
[LLM-based Rewriting of Inappropriate Argumentation using Reinforcement Learning from Machine Feedback](https://arxiv.org/abs/2406.03363) (Ziegenbein et al., ACL 2024)