Added dataset card

README.md

@@ -1,3 +1,159 @@
 ---
-license: cc-by-sa-3.0
+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:[email protected])
+
+### 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 [[email protected]](mailto:[email protected]).
+
+### 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",
+}
+```

mt_geneval.py

@@ -211,10 +211,10 @@ class WmtVat(datasets.GeneratorBasedBuilder):
                     "reference_feminine_annotated": rfa,
                     "source_masculine_annotated": sma,
                     "reference_masculine_annotated": rma,
-                    "source_feminine_keywords": sfk,
-                    "reference_feminine_keywords": rfk,
-                    "source_masculine_keywords": smk,
-                    "reference_masculine_keywords": rmk
+                    "source_feminine_keywords": ";".join(sfk),
+                    "reference_feminine_keywords": ";".join(rfk),
+                    "source_masculine_keywords": ";".join(smk),
+                    "reference_masculine_keywords": ";".join(rmk)
                 }
         else:
             with open(filepaths["2to1"]) as f: