--- annotations_creators: - expert-generated language: - en - it - fr - ar - de - hi - pt - ru - es language_creators: - expert-generated license: - cc-by-sa-3.0 multilinguality: - translation pretty_name: mt_geneval size_categories: - 1K {Arabic, French, German, Hindi, Italian, Portuguese, Russian, Spanish}. The dataset contains individual sentences with annotations on the gendered target words, and contrastive original-invertend translations with additional preceding context. **Disclaimer**: *The MT-GenEval benchmark was released in the EMNLP 2022 paper [MT-GenEval: A Counterfactual and Contextual Dataset for Evaluating Gender Accuracy in Machine Translation](https://arxiv.org/abs/2211.01355) by Anna Currey, Maria Nadejde, Raghavendra Pappagari, Mia Mayer, Stanislas Lauly, Xing Niu, Benjamin Hsu, and Georgiana Dinu and is hosted through Github by the [Amazon Science](https://github.com/amazon-science?type=source) organization. The dataset is licensed under a [Creative Commons Attribution-ShareAlike 3.0 Unported License](https://creativecommons.org/licenses/by-sa/3.0/).* ### Supported Tasks and Leaderboards #### Machine Translation Refer to the [original paper](https://arxiv.org/abs/2211.01355) for additional details on gender accuracy evaluation with MT-GenEval. ### Languages The dataset contains source English sentences extracted from Wikipedia translated into the following languages: Arabic (`ar`), French (`fr`), German (`de`), Hindi (`hi`), Italian (`it`), Portuguese (`pt`), Russian (`ru`), and Spanish (`es`). ## Dataset Structure ### Data Instances The dataset contains two configuration types, `sentences` and `context`, mirroring the original repository structure, with source and target language specified in the configuration name (e.g. `sentences_en_ar`, `context_en_it`) The `sentences` configurations contains masculine and feminine versions of individual sentences with gendered word annotations. Here is an example entry of the `sentences_en_it` split (all `sentences_en_XX` splits have the same structure): ```json { { "orig_id": 0, "source_feminine": "Pagratidis quickly recanted her confession, claiming she was psychologically pressured and beaten, and until the moment of her execution, she remained firm in her innocence.", "reference_feminine": "Pagratidis subito ritrattò la sua confessione, affermando che era aveva subito pressioni psicologiche e era stata picchiata, e fino al momento della sua esecuzione, rimase ferma sulla sua innocenza.", "source_masculine": "Pagratidis quickly recanted his confession, claiming he was psychologically pressured and beaten, and until the moment of his execution, he remained firm in his innocence.", "reference_masculine": "Pagratidis subito ritrattò la sua confessione, affermando che era aveva subito pressioni psicologiche e era stato picchiato, e fino al momento della sua esecuzione, rimase fermo sulla sua innocenza.", "source_feminine_annotated": "Pagratidis quickly recanted her confession, claiming she was psychologically pressured and beaten, and until the moment of her execution, she remained firm in her innocence.", "reference_feminine_annotated": "Pagratidis subito ritrattò la sua confessione, affermando che era aveva subito pressioni psicologiche e era stata picchiata, e fino al momento della sua esecuzione, rimase ferma sulla sua innocenza.", "source_masculine_annotated": "Pagratidis quickly recanted his confession, claiming he was psychologically pressured and beaten, and until the moment of his execution, he remained firm in his innocence.", "reference_masculine_annotated": "Pagratidis subito ritrattò la sua confessione, affermando che era aveva subito pressioni psicologiche e era stato picchiato, e fino al momento della sua esecuzione, rimase fermo sulla sua innocenza.", "source_feminine_keywords": "her;she;her;she;her", "reference_feminine_keywords": "stata picchiata;ferma", "source_masculine_keywords": "his;he;his;he;his", "reference_masculine_keywords": "stato picchiato;fermo", } } ``` The `context` configuration contains instead different English sources related to stereotypical professional roles with additional preceding context and contrastive original-inverted translations. Here is an example entry of the `context_en_it` split (all `context_en_XX` splits have the same structure): ```json { "orig_id": 0, "context": "Pierpont told of entering and holding up the bank and then fleeing to Fort Wayne, where the loot was divided between him and three others.", "source": "However, Pierpont stated that Skeer was the planner of the robbery.", "reference_original": "Comunque, Pierpont disse che Skeer era il pianificatore della rapina.", "reference_flipped": "Comunque, Pierpont disse che Skeer era la pianificatrice della rapina." } ``` ### Data Splits All `sentences_en_XX` configurations have 1200 examples in the `train` split and 300 in the `test` split. For the `context_en_XX` configurations, the number of example depends on the language pair: | Configuration | # Train | # Test | | :-----------: | :--------: | :-----: | | `context_en_ar` | 792 | 1100 | | `context_en_fr` | 477 | 1099 | | `context_en_de` | 598 | 1100 | | `context_en_hi` | 397 | 1098 | | `context_en_it` | 465 | 1904 | | `context_en_pt` | 574 | 1089 | | `context_en_ru` | 583 | 1100 | | `context_en_es` | 534 | 1096 | ### Dataset Creation From the original paper: >In developing MT-GenEval, our goal was to create a realistic, gender-balanced dataset that naturally incorporates a diverse range of gender phenomena. To this end, we extracted English source sentences from Wikipedia as the basis for our dataset. We automatically pre-selected relevant sentences using EN gender-referring words based on the list provided by [Zhao et al. (2018)](https://doi.org/10.18653/v1/N18-2003). Please refer to the original article [MT-GenEval: A Counterfactual and Contextual Dataset for Evaluating Gender Accuracy in Machine Translation](https://arxiv.org/abs/2211.01355) for additional information on dataset creation. ## Additional Information ### Dataset Curators The original authors of MT-GenEval are the curators of the original dataset. For problems or updates on this 🤗 Datasets version, please contact [gabriele.sarti996@gmail.com](mailto:gabriele.sarti996@gmail.com). ### Licensing Information The dataset is licensed under the [Creative Commons Attribution-ShareAlike 3.0 International License](https://creativecommons.org/licenses/by-sa/3.0/). ### Citation Information Please cite the authors if you use these corpora in your work. ```bibtex @inproceedings{currey-etal-2022-mtgeneval, title = "{MT-GenEval}: {A} Counterfactual and Contextual Dataset for Evaluating Gender Accuracy in Machine Translation", author = "Currey, Anna and Nadejde, Maria and Pappagari, Raghavendra and Mayer, Mia and Lauly, Stanislas, and Niu, Xing and Hsu, Benjamin and Dinu, Georgiana", booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing", month = dec, year = "2022", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/2211.01355", } ```