| --- |
| license: odc-by |
| language: |
| - en |
| tags: |
| - gender |
| - ambiguity |
| pretty_name: 'GAND: Gender Ambiguous Natural Data' |
| size_categories: |
| - 1K<n<10K |
| task_categories: |
| - translation |
| --- |
| |
| ## Dataset Description |
| GAND (Gender-Ambiguous Natural Data) is a benchmarking resource for evaluating gender (bias) in machine translation or downstream NLP tasks. |
| The data stems purely from natural data resources (OpenSubtitles from the [OPUS project](https://opus.nlpl.eu/) and [C4](https://huggingface.co/datasets/allenai/c4)). |
| The data has been meticulously (automatically + manually) filtered to ensure complete gender ambiguity with respect to a specific referent. |
| More information on the compilation of GAND can be found on [GitHub](https://github.com/jhacken/GAND/tree/main). |
|
|
| ## Usage |
| ```python |
| from datasets import load_dataset |
| ds = load_dataset("jhacken/GAND") |
| ``` |
|
|
| ## Train, dev, test split |
| - Train set: 4037 rows |
| - Dev set: 505 rows |
| - Test set: 505 rows |
|
|
| ## Dataset Structure |
| | referent | EN_source_sentence | referent_embedding | sentence_source | |
| |----------|--------------------|--------------------|-----------------| |
| | assistant | No one' s permitted to enter the library... other than myself and my assistant. | female_embedding_list | OpenSubtitles | |
| | specialist | As a social media specialist with a million things on your plate, you might not have been aware that citrus was all the rage atm. | LLM_neutral_list | C4 | |
|
|
| ## Cite this dataset |
| @dataset{hackenbuchner_2026_20324375, |
| author = {Hackenbuchner, Janiça and |
| Degraeuwe, Jasper and |
| Tezcan, Arda and |
| Daems, Joke}, |
| title = {GAND Dataset: Gender-Ambiguous Natural Data}, |
| month = may, |
| year = 2026, |
| publisher = {Zenodo}, |
| version = {v1.0.0}, |
| doi = {10.5281/zenodo.20324375}, |
| url = {https://doi.org/10.5281/zenodo.20324375}, |
| } |
| |
| ## Acknowledgements |
| GAND was developed as part of a strategic basic PhD research (1SH5V24N) fully funded by The Research Foundation – Flanders (FWO) for the time span of four years, |
| from 01.11.2023 until 31.10.2027, and hosted within the Language and Translation Technology Team (LT3) at Ghent University. |
| The computational resources (Stevin Supercomputer Infrastructure) and services used in this work were provided by the VSC (Flemish Supercomputer Center), |
| funded by Ghent University, FWO and the Flemish Government - department EWI. |
