Datasets:

WorkInTheDark
/

FairytaleQA

	# 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.
	# TODO: Address all TODOs and remove all explanatory comments

	"""FairytaleQA: An Authentic Dataset for Narrative Comprehension"""

	import csv
	import json
	import os

	import datasets

	logger = datasets.logging.get_logger(__name__)

	_CITATION = """\
	@inproceedings{xu-etal-2022-fantastic,
	title = "Fantastic Questions and Where to Find Them: {F}airytale{QA} {--} An Authentic Dataset for Narrative Comprehension",
	author = "Xu, Ying and
	Wang, Dakuo and
	Yu, Mo and
	Ritchie, Daniel and
	Yao, Bingsheng and
	Wu, Tongshuang and
	Zhang, Zheng and
	Li, Toby and
	Bradford, Nora and
	Sun, Branda and
	Hoang, Tran and
	Sang, Yisi and
	Hou, Yufang and
	Ma, Xiaojuan and
	Yang, Diyi and
	Peng, Nanyun and
	Yu, Zhou and
	Warschauer, Mark",
	booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
	month = may,
	year = "2022",
	address = "Dublin, Ireland",
	publisher = "Association for Computational Linguistics",
	url = "https://aclanthology.org/2022.acl-long.34",
	doi = "10.18653/v1/2022.acl-long.34",
	pages = "447--460",
	abstract = "Question answering (QA) is a fundamental means to facilitate assessment and training of narrative comprehension skills for both machines and young children, yet there is scarcity of high-quality QA datasets carefully designed to serve this purpose. In particular, existing datasets rarely distinguish fine-grained reading skills, such as the understanding of varying narrative elements. Drawing on the reading education research, we introduce FairytaleQA, a dataset focusing on narrative comprehension of kindergarten to eighth-grade students. Generated by educational experts based on an evidence-based theoretical framework, FairytaleQA consists of 10,580 explicit and implicit questions derived from 278 children-friendly stories, covering seven types of narrative elements or relations. Our dataset is valuable in two folds: First, we ran existing QA models on our dataset and confirmed that this annotation helps assess models{'} fine-grained learning skills. Second, the dataset supports question generation (QG) task in the education domain. Through benchmarking with QG models, we show that the QG model trained on FairytaleQA is capable of asking high-quality and more diverse questions.",
	}
	"""

	_DESCRIPTION = """\
	FairytaleQA dataset, an open-source dataset focusing on comprehension of narratives, \
	targeting students from kindergarten to eighth grade. The FairytaleQA dataset is \
	annotated by education experts based on an evidence-based theoretical framework. \
	It consists of 10,580 explicit and implicit questions derived from 278 children-friendly \
	stories, covering seven types of narrative elements or relations.
	"""
	_URLS = 'https://huggingface.co/datasets/WorkInTheDark/FairytaleQA/'

	class FairytaleQAConfig(datasets.BuilderConfig):
	"""BuilderConfig for FairytaleQA."""

	def __init__(self, **kwargs):
	"""BuilderConfig for FairytaleQA.
	Args:
	**kwargs: keyword arguments forwarded to super.
	"""
	super(FairytaleQAConfig, self).__init__(**kwargs)

	class FairytaleQA(datasets.GeneratorBasedBuilder):
	"""TODO: Short description of my dataset."""

	BUILDER_CONFIGS = [
	FairytaleQAConfig(
	name="plain_text",
	version=datasets.Version("1.0.0", ""),
	description="Plain text",
	),
	]

	def _info(self):
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=datasets.Features(
	{
	"story_name": datasets.Value("string"),
	"story_section": datasets.Value("string"),
	"question": datasets.Value("string"),
	"answer2": datasets.Value("string"),
	"local-or-sum": datasets.Value("string"),
	"attribute": datasets.Value("string"),
	"ex-or-im": datasets.Value("string"),
	"ex-or-im2": datasets.Value("string"),
	}
	),
	# No default supervised_keys (as we have to pass both question
	# and context as input).
	supervised_keys=None,
	citation=_CITATION,

	)

	def _split_generators(self, dl_manager):
	urls_to_download = _URLS
	downloaded_files = dl_manager.download_and_extract(urls_to_download)

	return [
	datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}),
	datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_files["valid"]}),
	datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["test"]}),
	]

	def _generate_examples(self, filepath):
	"""This function returns the examples in the raw (text) form."""
	logger.info("generating examples from = %s", filepath)
	with open(filepath) as file_obj:

	heading = next(file_obj)
	reader_obj = csv.reader(file_obj)
	key = 0
	for row in reader_obj:
	# print(row)
	story_name = row[0]
	story_section = row[1]
	question = row[2]
	answer1 = row[3]
	answer2 = row[4]
	local_or_sum = row[5]
	attribute = row[6]
	ex_or_im = row[7]
	ex_or_im2 = row[8]

	yield key, {
	'story_name': story_name,
	'story_section': story_section,
	'question': question,
	'answer1': answer1,
	'answer2': answer2,
	'local-or-sum': local_or_sum,
	'attribute': attribute,
	'ex-or-im': ex_or_im,
	'ex-or-im2': ex_or_im2,
	}
	key += 1