Datasets:
Tasks:
Token Classification
Sub-tasks:
coreference-resolution
Languages:
English
Multilinguality:
monolingual
Size Categories:
1K<n<10K
Language Creators:
expert-generated
Annotations Creators:
expert-generated
Source Datasets:
original
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File size: 16,054 Bytes
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# coding=utf-8
# Copyright 2020 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.
"""WinoBias: Winograd-schema dataset for detecting gender bias"""
import collections
import datasets
_CITATION = """\
@article{DBLP:journals/corr/abs-1804-06876,
author = {Jieyu Zhao and
Tianlu Wang and
Mark Yatskar and
Vicente Ordonez and
Kai{-}Wei Chang},
title = {Gender Bias in Coreference Resolution: Evaluation and Debiasing Methods},
journal = {CoRR},
volume = {abs/1804.06876},
year = {2018},
url = {http://arxiv.org/abs/1804.06876},
archivePrefix = {arXiv},
eprint = {1804.06876},
timestamp = {Mon, 13 Aug 2018 16:47:01 +0200},
biburl = {https://dblp.org/rec/journals/corr/abs-1804-06876.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
"""
_DESCRIPTION = """\
WinoBias, a Winograd-schema dataset for coreference resolution focused on gender bias.
The corpus contains Winograd-schema style sentences with entities corresponding to people
referred by their occupation (e.g. the nurse, the doctor, the carpenter).
"""
_HOMEPAGE = "https://uclanlp.github.io/corefBias/overview"
_LICENSE = "MIT License (https://github.com/uclanlp/corefBias/blob/master/LICENSE)"
_URL = "https://raw.githubusercontent.com/uclanlp/corefBias/master/WinoBias/wino/data/conll_format"
class WinoBiasConfig(datasets.BuilderConfig):
def __init__(self, **kwargs):
super(WinoBiasConfig, self).__init__(version=datasets.Version("1.0.0", ""), **kwargs)
class WinoBias(datasets.GeneratorBasedBuilder):
"""WinoBias: Winograd-schema dataset for detecting gender bias"""
# This is an example of a dataset with multiple configurations.
# If you don't want/need to define several sub-sets in your dataset,
# just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.
# If you need to make complex sub-parts in the datasets with configurable options
# You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
# BUILDER_CONFIG_CLASS = MyBuilderConfig
# You will be able to load one or the other configurations in the following list with
# data = datasets.load_dataset('my_dataset', 'first_domain')
# data = datasets.load_dataset('my_dataset', 'second_domain')
def __init__(self, *args, writer_batch_size=None, **kwargs):
super(WinoBias, self).__init__(*args, **kwargs)
# Batch size used by the ArrowWriter
# It defines the number of samples that are kept in memory before writing them
# and also the length of the arrow chunks
# None means that the ArrowWriter will use its default value
self._writer_batch_size = writer_batch_size or 100
BUILDER_CONFIGS = [
WinoBiasConfig(
name="type1_pro",
description="winoBias type1_pro_stereotype data in cornll format",
),
WinoBiasConfig(
name="type1_anti",
description="winoBias type1_anti_stereotype data in cornll format",
),
WinoBiasConfig(
name="type2_pro",
description="winoBias type2_pro_stereotype data in cornll format",
),
WinoBiasConfig(
name="type2_anti",
description="winoBias type2_anti_stereotype data in cornll format",
),
]
def _info(self):
return datasets.DatasetInfo(
# This is the description that will appear on the datasets page.
description=_DESCRIPTION,
# This defines the different columns of the dataset and their types
# Info about features for this: http://cemantix.org/data/ontonotes.html
features=datasets.Features(
{
"document_id": datasets.Value("string"),
"part_number": datasets.Value("string"),
"word_number": datasets.Sequence(datasets.Value("int32")),
"tokens": datasets.Sequence(datasets.Value("string")),
"pos_tags": datasets.Sequence(
datasets.features.ClassLabel(
names=[
'"',
"''",
"#",
"$",
"(",
")",
",",
".",
":",
"``",
"CC",
"CD",
"DT",
"EX",
"FW",
"IN",
"JJ",
"JJR",
"JJS",
"LS",
"MD",
"NN",
"NNP",
"NNPS",
"NNS",
"NN|SYM",
"PDT",
"POS",
"PRP",
"PRP$",
"RB",
"RBR",
"RBS",
"RP",
"SYM",
"TO",
"UH",
"VB",
"VBD",
"VBG",
"VBN",
"VBP",
"VBZ",
"WDT",
"WP",
"WP$",
"WRB",
"HYPH",
"XX",
"NFP",
"AFX",
"ADD",
"-LRB-",
"-RRB-",
"-",
]
)
),
"parse_bit": datasets.Sequence(datasets.Value("string")),
"predicate_lemma": datasets.Sequence(datasets.Value("string")),
"predicate_framenet_id": datasets.Sequence(datasets.Value("string")),
"word_sense": datasets.Sequence(datasets.Value("string")),
"speaker": datasets.Sequence(datasets.Value("string")),
"ner_tags": datasets.Sequence(
datasets.features.ClassLabel(
names=[
"B-PERSON",
"I-PERSON",
"B-NORP",
"I-NORP",
"B-FAC",
"I-FAC",
"B-ORG",
"I-ORG",
"B-GPE",
"I-GPE",
"B-LOC",
"I-LOC",
"B-PRODUCT",
"I-PRODUCT",
"B-EVENT",
"I-EVENT",
"B-WORK_OF_ART",
"I-WORK_OF_ART",
"B-LAW",
"I-LAW",
"B-LANGUAGE",
"I-LANGUAGE",
"B-DATE",
"I-DATE",
"B-TIME",
"I-TIME",
"B-PERCENT",
"I-PERCENT",
"B-MONEY",
"I-MONEY",
"B-QUANTITY",
"I-QUANTITY",
"B-ORDINAL",
"I-ORDINAL",
"B-CARDINAL",
"I-CARDINAL",
"*",
"0",
"-",
]
)
),
"verbal_predicates": datasets.Sequence(datasets.Value("string")),
"coreference_clusters": datasets.Sequence(datasets.Value("string")),
}
),
supervised_keys=None,
# Homepage of the dataset for documentation
homepage=_HOMEPAGE,
# License for the dataset if available
license=_LICENSE,
# Citation for the dataset
citation=_CITATION,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
dev_data_dir = dl_manager.download(_URL + "/dev_" + self.config.name + "_stereotype.v4_auto_conll")
test_data_dir = dl_manager.download(_URL + "/test_" + self.config.name + "_stereotype.v4_auto_conll")
return [
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
# These kwargs will be passed to _generate_examples
gen_kwargs={"filepath": dev_data_dir},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
# These kwargs will be passed to _generate_examples
gen_kwargs={"filepath": test_data_dir},
),
]
def _generate_examples(self, filepath):
"""Yields examples."""
with open(filepath, encoding="utf-8") as f:
id_ = 0
document_id = None
part_number = 0
word_num = []
tokens = []
pos_tags = []
parse_bit = []
predicate_lemma = []
predicate_framenet_id = []
word_sense = []
speaker = []
ner_tags = []
ner_start = False
verbal_predicates = []
coreference = []
clusters = collections.defaultdict(list)
coref_stacks = collections.defaultdict(list)
for line in f:
if line.startswith("#begin") or line.startswith("#end"):
continue
elif not line.strip():
id_ += 1
yield str(id_), {
"document_id": document_id,
"part_number": part_number,
"word_number": word_num,
"tokens": tokens,
"pos_tags": pos_tags,
"parse_bit": parse_bit,
"predicate_lemma": predicate_lemma,
"predicate_framenet_id": predicate_framenet_id,
"word_sense": word_sense,
"speaker": speaker,
"ner_tags": ner_tags,
"verbal_predicates": verbal_predicates,
"coreference_clusters": sum(
clusters[1], []
), # flatten the list as writing the exmaples needs an array.
}
word_num = []
tokens = []
pos_tags = []
parse_bit = []
predicate_lemma = []
predicate_framenet_id = []
word_sense = []
speaker = []
ner_tags = []
verbal_predicates = []
coreference = []
clusters = collections.defaultdict(list)
coref_stacks = collections.defaultdict(list)
else:
splits = [s for s in line.split() if s]
if len(splits) > 7:
document_id = splits[0]
part_number = splits[1]
word_num.append(splits[2])
tokens.append(splits[3])
pos_tags.append(splits[4])
parse_bit.append(splits[5])
predicate_lemma.append(splits[6])
predicate_framenet_id.append(splits[7])
word_sense.append(splits[8])
speaker.append(splits[9])
ner_word = splits[10]
coreference = splits[-1]
if ")" in ner_word and ner_start:
ner_start = False
ner_word = "0"
if "(" in ner_word:
ner_start = True
ner_word = ner_word.strip(" ").replace("(", "B-").replace("*", "").replace(")", "")
start_word = ner_word.strip(" ").replace("B-", "")
if ner_start:
if ner_word.strip(" ") == "*":
ner_word = "I-" + start_word
ner_tags.append(ner_word)
word_is_verbal_predicate = any(["(V" in x for x in splits[11:-1]])
if word_is_verbal_predicate:
verbal_predicates.append(splits[3])
if coreference != "-":
for segment in coreference.split("|"):
if segment[0] == "(":
if segment[-1] == ")":
cluster_id = int(segment[1:-1])
clusters[cluster_id].append([splits[2], splits[2]])
else:
cluster_id = int(segment[1:])
coref_stacks[cluster_id].append(splits[2])
else:
cluster_id = int(segment[:-1])
start = coref_stacks[cluster_id].pop()
clusters[cluster_id].append([start, splits[2]])
if tokens:
# add the last one
id_ += 1
yield str(id_), {
"document_id": document_id,
"part_number": part_number,
"word_number": word_num,
"tokens": tokens,
"pos_tags": pos_tags,
"parse_bit": parse_bit,
"predicate_lemma": predicate_lemma,
"predicate_framenet_id": predicate_framenet_id,
"word_sense": word_sense,
"speaker": speaker,
"ner_tags": ner_tags,
"verbal_predicates": verbal_predicates,
"coreference_clusters": sum(clusters[1], []),
}
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