Datasets:
File size: 10,685 Bytes
40be566 d865c72 40be566 2f9ef10 40be566 d865c72 40be566 d865c72 40be566 d865c72 40be566 d865c72 40be566 2f9ef10 40be566 7a379d5 40be566 d865c72 40be566 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 |
# 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
} |