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README.md

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----
-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",
-}
-```

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mt_geneval.py

@@ -1,236 +0,0 @@
-# coding=utf-8
-# Copyright 2022 The HuggingFace Datasets Authors and the current dataset script contributor.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""MT-GenEval: A Counterfactual and Contextual Dataset for Evaluating Gender Accuracy in Machine Translation"""
-
-import re
-from pathlib import Path
-from typing import Dict
-
-import datasets
-from datasets.utils.download_manager import DownloadManager
-
-
-_CITATION = """\
-@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/pdf/2211.01355.pdf,
-}
-"""
-
-_DESCRIPTION = """\
-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.
-"""
-
-_HOMEPAGE = "https://github.com/amazon-science/machine-translation-gender-eval"
-
-_LICENSE = "Creative Commons Attribution Share Alike 3.0"
-
-_URL = "https://raw.githubusercontent.com/amazon-science/machine-translation-gender-eval/main/data"
-
-_CONFIGS = ["sentences", "context"]
-_LANGS = ["ar", "fr", "de", "hi", "it", "pt", "ru", "es"]
-
-rexf = re.compile('<F>(.+?)</F>')
-rexm = re.compile('<M>(.+?)</M>')
-
-class MTGenEvalConfig(datasets.BuilderConfig):
-    def __init__(
-        self,
-        data_type: str,
-        source_language: str, 
-        target_language: str,
-        **kwargs
-    ):
-        """BuilderConfig for MT-GenEval.
-
-        Args:
-            source_language: `str`, source language for translation.
-            target_language: `str`, translation language.
-            **kwargs: keyword arguments forwarded to super.
-        """
-        super().__init__(**kwargs)
-        self.data_type = data_type
-        self.source_language = source_language
-        self.target_language = target_language
-
-
-class WmtVat(datasets.GeneratorBasedBuilder):
-
-    VERSION = datasets.Version("1.0.0")
-
-    BUILDER_CONFIGS = [
-        MTGenEvalConfig(
-            name=f"{cfg}_en_{lang}",
-            data_type=cfg,
-            source_language="en",
-            target_language=lang,
-        ) for lang in _LANGS for cfg in _CONFIGS
-    ]
-
-    def _info(self):
-        if self.config.name.startswith("sentences"):
-            features = datasets.Features(
-                {
-                    "orig_id": datasets.Value("int32"),
-                    "source_feminine": datasets.Value("string"),
-                    "reference_feminine": datasets.Value("string"),
-                    "source_masculine": datasets.Value("string"),
-                    "reference_masculine": datasets.Value("string"),
-                    "source_feminine_annotated": datasets.Value("string"),
-                    "reference_feminine_annotated": datasets.Value("string"),
-                    "source_masculine_annotated": datasets.Value("string"),
-                    "reference_masculine_annotated": datasets.Value("string"),
-                    "source_feminine_keywords": datasets.Value("string"),
-                    "reference_feminine_keywords": datasets.Value("string"),
-                    "source_masculine_keywords": datasets.Value("string"),
-                    "reference_masculine_keywords": datasets.Value("string")
-                }
-            )
-        else:
-            features = datasets.Features(
-                {
-                    "orig_id": datasets.Value("int32"),
-                    "context": datasets.Value("string"),
-                    "source": datasets.Value("string"),
-                    "reference_original": datasets.Value("string"),
-                    "reference_flipped": datasets.Value("string")
-                }
-            )
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=features,
-            homepage=_HOMEPAGE,
-            license=_LICENSE,
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager: DownloadManager):
-        """Returns SplitGenerators."""
-        base_path = f"{_URL}/{self.config.data_type}"
-        filepaths = {}
-        for split in ["dev", "test"]:
-            filepaths[split] = {}
-            if self.config.name.startswith("sentences"):
-                for curr_lang in [self.config.source_language, self.config.target_language]:
-                    for gender in ["feminine", "masculine"]:
-                        fname = f"geneval-sentences-{gender}-{split}.en_{self.config.target_language}.{curr_lang}"
-                        langname = "source" if curr_lang == self.config.source_language else "reference"
-                        url = f"{base_path}/{split}/{fname}"
-                        filepaths[split][f"{langname}_{gender}"] = dl_manager.download_and_extract(url)
-                        annotated_url = f"{base_path}/{split}/annotated/{fname}"
-                        filepaths[split][f"{langname}_{gender}_annotated"] = dl_manager.download_and_extract(annotated_url)
-            else:
-                ftypes = ["2to1", "original", "flipped"]
-                for ftype in ftypes:
-                    curr_lang = self.config.source_language if ftype == "2to1" else self.config.target_language
-                    fname = f"geneval-context-wikiprofessions-{ftype}-{split}.en_{self.config.target_language}.{curr_lang}"
-                    url = f"{base_path}/{fname}"
-                    filepaths[split][ftype] = dl_manager.download_and_extract(url)
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={
-                    "filepaths": filepaths["dev"],
-                    "cfg_name": self.config.data_type
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                gen_kwargs={
-                    "filepaths": filepaths["test"],
-                    "cfg_name": self.config.data_type
-                },
-            ),
-        ]
-
-
-    def _generate_examples(
-        self, filepaths: Dict[str, str], cfg_name: str
-    ):
-        """ Yields examples as (key, example) tuples. """
-        if cfg_name == "sentences":
-            with open(filepaths["source_feminine"]) as f:
-                source_feminine = f.read().splitlines()
-            with open(filepaths["reference_feminine"]) as f:
-                reference_feminine = f.read().splitlines()
-            with open(filepaths["source_masculine"]) as f:
-                source_masculine = f.read().splitlines()
-            with open(filepaths["reference_masculine"]) as f:
-                reference_masculine = f.read().splitlines()
-            with open(filepaths["source_feminine_annotated"]) as f:
-                source_feminine_annotated = f.read().splitlines()
-            with open(filepaths["reference_feminine_annotated"]) as f:
-                reference_feminine_annotated = f.read().splitlines()
-            with open(filepaths["source_masculine_annotated"]) as f:
-                source_masculine_annotated = f.read().splitlines()
-            with open(filepaths["reference_masculine_annotated"]) as f:
-                reference_masculine_annotated = f.read().splitlines()
-            source_feminine_keywords = [rexf.findall(s) for s in source_feminine_annotated]
-            reference_feminine_keywords = [rexf.findall(s) for s in reference_feminine_annotated]
-            source_masculine_keywords = [rexm.findall(s) for s in source_masculine_annotated]
-            reference_masculine_keywords = [rexm.findall(s) for s in reference_masculine_annotated]
-            for i, (sf, rf, sm, rm, sfa, rfa, sma, rma, sfk, rfk, smk, rmk) in enumerate(
-                zip(
-                    source_feminine, reference_feminine, source_masculine, reference_masculine,
-                    source_feminine_annotated, reference_feminine_annotated, source_masculine_annotated, reference_masculine_annotated,
-                    source_feminine_keywords, reference_feminine_keywords, source_masculine_keywords, reference_masculine_keywords
-                )
-            ):
-                yield i, {
-                    "orig_id": i,
-                    "source_feminine": sf,
-                    "reference_feminine": rf,
-                    "source_masculine": sm,
-                    "reference_masculine": rm,
-                    "source_feminine_annotated": sfa,
-                    "reference_feminine_annotated": rfa,
-                    "source_masculine_annotated": sma,
-                    "reference_masculine_annotated": rma,
-                    "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:
-                context_and_source = f.read().splitlines()
-            with open(filepaths["original"]) as f:
-                orig_ref = f.read().splitlines()
-            with open(filepaths["flipped"]) as f:
-                flipped_ref = f.read().splitlines()
-            context = [s.split("<sep>")[0].strip() for s in context_and_source]
-            source = [s.split("<sep>")[1].strip() for s in context_and_source]
-            for i, (c, s, oref, fref) in enumerate(zip(context, source, orig_ref, flipped_ref)):
-                yield i, {
-                    "orig_id": i,
-                    "context": c,
-                    "source": s,
-                    "reference_original": oref,
-                    "reference_flipped": fref
-                }

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