--- license: cc-by-4.0 task_categories: - text-classification - zero-shot-classification task_ids: - multi-label-classification language: - en tags: - Human Values - Value Detection - Multi-Label pretty_name: Human Value Detection Dataset size_categories: - 1K`: As the [Task](https://touche.webis.de/semeval23/touche23-web/) is focused mainly on the detection of value-categories, each base configuration ([listed below](#p-list-base-configs)) has the 20 value-categories as labels: ```python labels = ["Self-direction: thought", "Self-direction: action", "Stimulation", "Hedonism", "Achievement", "Power: dominance", "Power: resources", "Face", "Security: personal", "Security: societal", "Tradition", "Conformity: rules", "Conformity: interpersonal", "Humility", "Benevolence: caring", "Benevolence: dependability", "Universalism: concern", "Universalism: nature", "Universalism: tolerance", "Universalism: objectivity"] ``` - `-level1`: The 54 human values from the level 1 of the value taxonomy are not used for the 2023 task (except for the annotation), but are still listed here for some might find them useful for understanding the value categories. Their order is also the same as in the original files. For more details see the [value-categories](#metadata-instances) configuration.

The configuration names (as replacements for <config>) in this dataset are:

- `main`: 8865 arguments (sources: `A`, `D`, `E`) with splits `train`, `validation`, and `test` (default configuration name) ```python dataset_main_train = load_dataset("webis/Touche23-ValueEval", split="train") dataset_main_validation = load_dataset("webis/Touche23-ValueEval", split="validation") dataset_main_test = load_dataset("webis/Touche23-ValueEval", split="test") ``` - `nahjalbalagha`: 279 arguments (source: `F`) with split `test` ```python dataset_nahjalbalagha_test = load_dataset("webis/Touche23-ValueEval", name="nahjalbalagha", split="test") ``` - `nyt`: 80 arguments (source: `G`) with split `test` ```python dataset_nyt_test = load_dataset("webis/Touche23-ValueEval", name="nyt", split="test") ``` - `zhihu`: 100 arguments (source: `C`) with split `validation` ```python dataset_zhihu_validation = load_dataset("webis/Touche23-ValueEval", name="zhihu", split="validation") ``` Please note that due to copyright reasons, there currently does not exist a direct download link to the arguments contained in the New york Times dataset. Accessing any of the `nyt` or `nyt-level1` configurations will therefore use the specifically created [nyt-downloader program](https://github.com/touche-webis-de/touche-code/tree/main/semeval23/human-value-detection/nyt-downloader) to create and access the arguments locally. See the program's [README](https://github.com/touche-webis-de/touche-code/blob/main/semeval23/human-value-detection/nyt-downloader/README.md) for further details. ### Metadata Instances The following lists all configuration names for metadata. Each configuration only has a single split named `meta`. - `ibm-meta`: Each row corresponds to one argument (IDs starting with `A`) from the [IBM-ArgQ-Rank-30kArgs](https://research.ibm.com/haifa/dept/vst/debating_data.shtml#Argument%20Quality) - `Argument ID`: The unique identifier for the argument - `WA`: the quality label according to the weighted-average scoring function - `MACE-P`: the quality label according to the MACE-P scoring function - `stance_WA`: the stance label according to the weighted-average scoring function - `stance_WA_conf`: the confidence in the stance label according to the weighted-average scoring function ```python dataset_ibm_metadata = load_dataset("webis/Touche23-ValueEval", name="ibm-meta", split="meta") ``` - `zhihu-meta`: Each row corresponds to one argument (IDs starting with `C`) from the Chinese question-answering website [Zhihu](https://www.zhihu.com) - `Argument ID`: The unique identifier for the argument - `Conclusion Chinese`: The original chinese conclusion statement - `Premise Chinese`: The original chinese premise statement - `URL`: Link to the original statement the argument was taken from ```python dataset_zhihu_metadata = load_dataset("webis/Touche23-ValueEval", name="zhihu-meta", split="meta") ``` - `gdi-meta`: Each row corresponds to one argument (IDs starting with `D`) from [GD IDEAS](https://www.groupdiscussionideas.com/) - `Argument ID`: The unique identifier for the argument - `URL`: Link to the topic the argument was taken from ```python dataset_gdi_metadata = load_dataset("webis/Touche23-ValueEval", name="gdi-meta", split="meta") ``` - `cofe-meta`: Each row corresponds to one argument (IDs starting with `E`) from [the Conference for the Future of Europe](https://futureu.europa.eu) - `Argument ID`: The unique identifier for the argument - `URL`: Link to the comment the argument was taken from ```python dataset_cofe_metadata = load_dataset("webis/Touche23-ValueEval", name="cofe-meta", split="meta") ``` - `nahjalbalagha-meta`: Each row corresponds to one argument (IDs starting with `F`). This file contains information on the 279 arguments in `nahjalbalagha` (or `nahjalbalagha-level1`) and 1047 additional arguments that were not labeled so far. This data was contributed by the language.ml lab. - `Argument ID`: The unique identifier for the argument - `Conclusion Farsi`: Conclusion text of the argument in Farsi - `Stance Farsi`: Stance of the `Premise` towards the `Conclusion`, in Farsi - `Premise Farsi`: Premise text of the argument in Farsi - `Conclusion English`: Conclusion text of the argument in English (translated from Farsi) - `Stance English`: Stance of the `Premise` towards the `Conclusion`; one of "in favor of", "against" - `Premise English`: Premise text of the argument in English (translated from Farsi) - `Source`: Source text of the argument; one of "Nahj al-Balagha", "Ghurar al-Hikam wa Durar ak-Kalim"; their Farsi translations were used - `Method`: How the premise was extracted from the source; one of "extracted" (directly taken), "deduced"; the conclusion are deduced ```python dataset_nahjalbalagha_metadata = load_dataset("webis/Touche23-ValueEval", name="nahjalbalagha-meta", split="meta") ``` - `nyt-meta`: Each row corresponds to one argument (IDs starting with `G`) from [The New York Times](https://www.nytimes.com) - `Argument ID`: The unique identifier for the argument - `URL`: Link to the article the argument was taken from - `Internet Archive timestamp`: Timestamp of the article's version in the Internet Archive that was used ```python dataset_nyt_metadata = load_dataset("webis/Touche23-ValueEval", name="nyt-meta", split="meta") ``` - `value-categories`: Contains a single JSON-entry with the structure of level 2 and level 1 values regarding the value taxonomy: ``` { "": { "": [ "", ... ], ... }, ... } ``` As this configuration contains just a single entry, an example usage could be: ```python value_categories = load_dataset("webis/Touche23-ValueEval", name="value-categories", split="meta")[0] ``` ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [Creative Commons Attribution 4.0 International (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/) ### Citation Information ``` @Article{mirzakhmedova:2023a, author = {Nailia Mirzakhmedova and Johannes Kiesel and Milad Alshomary and Maximilian Heinrich and Nicolas Handke\ and Xiaoni Cai and Valentin Barriere and Doratossadat Dastgheib and Omid Ghahroodi and {Mohammad Ali} Sadraei\ and Ehsaneddin Asgari and Lea Kawaletz and Henning Wachsmuth and Benno Stein}, doi = {10.48550/arXiv.2301.13771}, journal = {CoRR}, month = jan, publisher = {arXiv}, title = {{The Touch{\'e}23-ValueEval Dataset for Identifying Human Values behind Arguments}}, volume = {abs/2301.13771}, year = 2023 } ```