# Touché23-Human-Value-Detection

Dataset for [Touché / SemEval 2023 Task 4; ValueEval: Identification of Human Values behind Arguments](https://touche.webis.de/semeval23/touche23-web). Based on the original [Webis-ArgValues-22 dataset](https://doi.org/10.5281/zenodo.5657249) accompanying the paper [Identifying the Human Values behind Arguments](https://webis.de/publications.html#kiesel_2022b), published at ACL'22.

The dataset currently contains 5220 arguments. We are, however, looking for more argument datasets (conclusion + stance + premise) to annotate and incorporate, especially datasets from different cultures and genres. Please send suggestions to our [task](mailto:valueeval@googlegroups.com) or [organizers](mailto:valueeval-organizers@googlegroups.com) mailing lists.


## Argument Corpus
The annotated corpus in tab-separated value format. Future versions of this dataset will contain more arguments and be split into "-training", "-validation", and "-testing" files to represent the corresponding sets for the evaluation.
- `arguments-training.tsv`: Each row corresponds to one argument
    - `Argument ID`: The unique identifier for the argument
    - `Conclusion`: Conclusion text of the argument
    - `Stance`: Stance of the `Premise` towards the `Conclusion`; one of "in favor of", "against"
    - `Premise`: Premise text of the argument
- `labels-training.tsv`: Each row corresponds to one argument
    - `Argument ID`: The unique identifier for the argument
    - Other: Each other column corresponds to one value category, with a 1 meaning that the argument resorts to the value category and a 0 that not
- `level1-labels-training.tsv`: The same as `labels-training.tsv` but for the 54 level 1 values of the taxonomy (used in human annotation). Though not directly part of the 2023 task, participants can still use them in their approaches.


## Value Taxonomy
Our proposed [value taxonomy](values.json) of 54 values in JSON format. The 2023 task employs level 2 only.
```
{
  "values": [
    {
      "alternativeName": "<the original name of each value from the literature>",
      "name": "<the name of each value as used in the crowdsourcing study>,
      "id": "<a unique 4-character identifier for the value>",
      "level2": "<value category this value belongs to>",
      "level3": [ "Openness to change", "Self-enhancement", "Conservation", "Self-transcendence" ], # one or two of these; to which this value belongs to
      "level4a": [ "Social focus", "Personal focus" ], # one of these; to which this value belongs to
      "level4b": [ "Growth, Anxiety-free", "Self-protection, Anxiety avoidance" ], # one or both of these; to which this value belongs to
      "descriptions": [
        "<example effect that a matching arguments might imply>",
        ...
      ],
      "related": [ # optional
        {
          "id": "<identifier of the related value>",
          "descriptions": [
            "<example effect that one might associate with this value, but rather indicates the related value>",
            ...
          ]
        }, ...
      ]
    }, ...
  ]
}
```


## Authors
- Johannes Kiesel, Bauhaus-Universität Weimar, johannes.kiesel@uni-weimar.de
- Milad Alshomary, Paderborn University, milad.alshomary@upb.de
- Nicolas Handke, Universität Leipzig, nh54fapa@studserv.uni-leipzig.de
- Xiaoni Cai, Technische Universität München, caix@in.tum.de
- Henning Wachsmuth, Paderborn University, henningw@upb.de
- Benno Stein, Bauhaus-Universität Weimar, benno.stein@uni-weimar.de


## License
This dataset is distributed under [CC BY-SA 4.0](http://creativecommons.org/licenses/by-sa/4.0/).

