Datasets:
Tasks:
Question Answering
Sub-tasks:
multiple-choice-qa
Languages:
English
Multilinguality:
monolingual
Size Categories:
10K<n<100K
Source Datasets:
original
ArXiv:
Tags:
License:
File size: 3,996 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.
"""MC-TACO Dataset."""
import csv
import datasets
_CITATION = """\
@inproceedings{ZKNR19,
author = {Ben Zhou, Daniel Khashabi, Qiang Ning and Dan Roth},
title = {“Going on a vacation” takes longer than “Going for a walk”: A Study of Temporal Commonsense Understanding },
booktitle = {EMNLP},
year = {2019},
}
"""
_DESCRIPTION = """\
MC-TACO (Multiple Choice TemporAl COmmonsense) is a dataset of 13k question-answer
pairs that require temporal commonsense comprehension. A system receives a sentence
providing context information, a question designed to require temporal commonsense
knowledge, and multiple candidate answers. More than one candidate answer can be plausible.
The task is framed as binary classification: givent he context, the question,
and the candidate answer, the task is to determine whether the candidate
answer is plausible ("yes") or not ("no")."""
_LICENSE = "Unknown"
_URLs = {
"dev": "https://raw.githubusercontent.com/CogComp/MCTACO/master/dataset/dev_3783.tsv",
"test": "https://raw.githubusercontent.com/CogComp/MCTACO/master/dataset/test_9442.tsv",
}
class McTaco(datasets.GeneratorBasedBuilder):
"""MC-TACO Dataset: temporal commonsense knowledge."""
VERSION = datasets.Version("1.1.0")
BUILDER_CONFIGS = [
datasets.BuilderConfig(
name="plain_text",
description="Plain text",
version=VERSION,
),
]
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"sentence": datasets.Value("string"),
"question": datasets.Value("string"),
"answer": datasets.Value("string"),
"label": datasets.ClassLabel(names=["no", "yes"]),
"category": datasets.ClassLabel(
names=["Event Duration", "Event Ordering", "Frequency", "Typical Time", "Stationarity"]
),
}
),
supervised_keys=None,
homepage="https://cogcomp.seas.upenn.edu/page/resource_view/125",
license=_LICENSE,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
data_dir = dl_manager.download_and_extract(_URLs)
return [
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"filepath": data_dir["test"],
},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={
"filepath": data_dir["dev"],
},
),
]
def _generate_examples(self, filepath):
"""Yields examples."""
with open(filepath, encoding="utf-8") as csv_file:
csv_reader = csv.reader(
csv_file,
delimiter="\t",
)
for id_, row in enumerate(csv_reader):
yield id_, {
"sentence": row[0],
"question": row[1],
"answer": row[2],
"label": row[3],
"category": row[4],
}
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