Datasets:
Tasks:
Question Answering
Sub-tasks:
multiple-choice-qa
Languages:
English
Multilinguality:
monolingual
Size Categories:
1K<n<10K
Language Creators:
found
Annotations Creators:
expert-generated
License:
File size: 8,490 Bytes
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# coding=utf-8
# Copyright 2020 The HuggingFace Datasets Authors.
#
# 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.
"""MCTest: Machine comprehension test: http://research.microsoft.com/mct"""
import os
import datasets
_CITATION = """\
@inproceedings{richardson-etal-2013-mctest,
title = "{MCT}est: A Challenge Dataset for the Open-Domain Machine Comprehension of Text",
author = "Richardson, Matthew and
Burges, Christopher J.C. and
Renshaw, Erin",
booktitle = "Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2013",
address = "Seattle, Washington, USA",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/D13-1020",
pages = "193--203",
}
"""
_DESCRIPTION = """\
MCTest requires machines to answer multiple-choice reading comprehension questions about fictional stories, directly tackling the high-level goal of open-domain machine comprehension.
"""
_HOMEPAGE = "https://www.aclweb.org/anthology/D13-1020/"
_DATA_URL = "http://parl.ai/downloads/mctest/mctest.tar.gz"
class MCTest(datasets.GeneratorBasedBuilder):
"""MCTest: Machine comprehension test: http://research.microsoft.com/mct"""
VERSION = datasets.Version("1.0.0")
BUILDER_CONFIGS = [
datasets.BuilderConfig(
name="mc500",
version=VERSION,
description="MC 500",
),
datasets.BuilderConfig(
name="mc160",
version=VERSION,
description="MC 160",
),
]
DEFAULT_CONFIG_NAME = "mc500"
def _info(self):
if self.config.name == "mc500":
features = datasets.Features(
{
"idx": dict(
{"story": datasets.Value("string"),
"question": datasets.Value("int32")
}
),
"question": datasets.Value("string"),
"story": datasets.Value("string"),
"properties": dict(
{
"author": datasets.Value("string"),
"work_time": datasets.Value("int32"),
"quality_score": datasets.Value("int32"),
"creativity_words": datasets.Sequence(datasets.Value("string")),
}
),
"answer_options": dict(
{
"A": datasets.Value("string"),
"B": datasets.Value("string"),
"C": datasets.Value("string"),
"D": datasets.Value("string")
}
),
"answer": datasets.Value("string"),
"question_is_multiple": datasets.Value("bool")
}
)
else:
features = datasets.Features(
{
"idx": dict(
{"story": datasets.Value("string"),
"question": datasets.Value("int32")
}
),
"question": datasets.Value("string"),
"story": datasets.Value("string"),
"properties": dict(
{
"author": datasets.Value("string"),
"work_time": datasets.Value("int32"),
}
),
"answer_options": dict(
{
"A": datasets.Value("string"),
"B": datasets.Value("string"),
"C": datasets.Value("string"),
"D": datasets.Value("string")
}
),
"answer": datasets.Value("string"),
"question_is_multiple": datasets.Value("bool")
}
)
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
homepage=_HOMEPAGE,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
data_dir = os.path.join(dl_manager.download_and_extract(_DATA_URL), 'mctest')
paths = {}
for phase in ["train", "dev", "test"]:
paths[phase] = {
"data": os.path.join(data_dir, "MCTest", f"{self.config.name}.{phase}.tsv"),
"answer": os.path.join(data_dir, "MCTest", f"{self.config.name}.{phase}.ans")
}
paths["test"]["answer"] = os.path.join(data_dir, "MCTestAnswers", f"{self.config.name}.test.ans")
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={"filepath": paths["train"]},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={"filepath": paths["dev"]},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={"filepath": paths["test"]},
),
]
def _get_properties(self, property_str):
"""
properties is a semicolon-delimited list of property:value pairs, including
Author (anonymized author id, consistent across all files)
Work Time(s): Seconds between author accepting and submitting the task
Qual. score: The author's grammar qualification test score (% correct)
Creativity Words: Words the author was given to encourage creativity
(there are no creativity words or qual score for mc160, see paper)
:param property_str:
:return:
"""
properties = property_str.split(';')
property_data = {
"author": properties[0].split(':')[-1].strip(),
"work_time": int(properties[1].split(':')[-1].strip())
}
if self.config.name == "mc500":
property_data.update(
{
"quality_score": int(properties[2].split(':')[-1].strip()),
"creativity_words": properties[3].split(':')[-1].strip().split(',')
}
)
return property_data
def _generate_examples(self, filepath):
tab_char = '\t'
data_path = filepath['data']
ans_path = filepath['answer']
data_lines = open(data_path, encoding="utf-8").read().split('\n')[:-1]
answer_lines = open(ans_path, encoding="utf-8").read().split('\n')[:-1]
for data_line, answer_line in zip(data_lines, answer_lines):
data_line_split = data_line.split(tab_char)
story_id = data_line_split[0]
properties = self._get_properties(data_line_split[1])
story = data_line_split[2]
answers = answer_line.split('\t')
data_line_split = data_line_split[3:]
for i in range(4):
answer = answers[i]
index = i*5
multiple, question_text = [x.strip() for x in data_line_split[index].strip().split(':')]
question_is_multiple = True if multiple == "multiple" else False
answer_options = {x: y for x, y in zip(["A", "B", "C", "D"], data_line_split[index+1:index+5])}
yield f"{story_id}-{i}", {
"idx":
{"story": story_id,
"question": i
},
"question": question_text,
"story": story,
"properties": properties,
"answer_options": answer_options,
"answer": answer,
"question_is_multiple": question_is_multiple
}
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