# 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"] r = re.compile('

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') 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 = [r.findall(s) for s in source_feminine_annotated] reference_feminine_keywords = [r.findall(s) for s in reference_feminine_annotated] source_masculine_keywords = [r.findall(s) for s in source_masculine_annotated] reference_masculine_keywords = [r.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("")[0].strip() for s in context_and_source] source = [s.split("")[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 }