| | --- |
| | 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<n<10K |
| | source_datasets: |
| | - original |
| | tags: |
| | - gender |
| | - constrained mt |
| | task_categories: |
| | - translation |
| | task_ids: [] |
| | --- |
| | |
| | # Dataset Card for MT-GenEval |
| |
|
| | ## Table of Contents |
| |
|
| | - [Dataset Card for MT-GenEval](#dataset-card-for-mt-geneval) |
| | - [Table of Contents](#table-of-contents) |
| | - [Dataset Description](#dataset-description) |
| | - [Dataset Summary](#dataset-summary) |
| | - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) |
| | - [Machine Translation](#machine-translation) |
| | - [Languages](#languages) |
| | - [Dataset Structure](#dataset-structure) |
| | - [Data Instances](#data-instances) |
| | - [Data Splits](#data-splits) |
| | - [Dataset Creation](#dataset-creation) |
| | - [Additional Information](#additional-information) |
| | - [Dataset Curators](#dataset-curators) |
| | - [Licensing Information](#licensing-information) |
| | - [Citation Information](#citation-information) |
| |
|
| | ## Dataset Description |
| |
|
| | - **Repository:** [Github](https://github.com/amazon-science/machine-translation-gender-eval) |
| | - **Paper:** [EMNLP 2022](https://arxiv.org/abs/2211.01355) |
| | - **Point of Contact:** [Anna Currey](mailto:ancurrey@amazon.com) |
| |
|
| | ### Dataset Summary |
| |
|
| | The MT-GenEval benchmark evaluates gender translation accuracy on English -> {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 <F>her</F> confession, claiming <F>she</F> was psychologically pressured and beaten, and until the moment of <F>her</F> execution, <F>she</F> remained firm in <F>her</F> innocence.", |
| | "reference_feminine_annotated": "Pagratidis subito ritrattò la sua confessione, affermando che era aveva subito pressioni psicologiche e era <F>stata picchiata</F>, e fino al momento della sua esecuzione, rimase <F>ferma</F> sulla sua innocenza.", |
| | "source_masculine_annotated": "Pagratidis quickly recanted <M>his</M> confession, claiming <M>he</M> was psychologically pressured and beaten, and until the moment of <M>his</M> execution, <M>he</M> remained firm in <M>his</M> innocence.", |
| | "reference_masculine_annotated": "Pagratidis subito ritrattò la sua confessione, affermando che era aveva subito pressioni psicologiche e era <M>stato picchiato</M>, e fino al momento della sua esecuzione, rimase <M>fermo</M> 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", |
| | } |
| | ``` |
