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# 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('<p>(.+?)</p>')
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("<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
} |