Datasets:
inquisitive_qg

	# coding=utf-8
	# Copyright 2020 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
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	# Lint as: python3
	"""Inquisitive Question Generation for High Level Text Comprehension"""


	import itertools

	import datasets


	_CITATION = """\
	@InProceedings{ko2020inquisitive,
	author = {Ko, Wei-Jen and Chen, Te-Yuan and Huang, Yiyan and Durrett, Greg and Li, Junyi Jessy},
	title = {Inquisitive Question Generation for High Level Text Comprehension},
	booktitle = {Proceedings of EMNLP},
	year = {2020},
	}
	"""

	_DESCRIPTION = """\
	A dataset of about 20k questions that are elicited from readers as they naturally read through a document sentence by sentence. \
	Compared to existing datasets, INQUISITIVE questions target more towards high-level (semantic and discourse) comprehension of text. \
	Because these questions are generated while the readers are processing the information, the questions directly communicate gaps between \
	the reader’s and writer’s knowledge about the events described in the text, and are not necessarily answered in the document itself. \
	This type of question reflects a real-world scenario: if one has questions during reading, some of them are answered by the text later on, \
	the rest are not, but any of them would help further the reader’s understanding at the particular point when they asked it. \
	This resource could enable question generation models to simulate human-like curiosity and cognitive processing, which may open up a new realm of applications.
	"""

	_ARTICLES_URL = "https://github.com/wjko2/INQUISITIVE/raw/master/articles.tgz"
	_QUESTIONS_URL = "https://github.com/wjko2/INQUISITIVE/raw/master/questions.txt"

	ALL_ARTICLE_IDS = list(range(1, 1501))
	DEV_ARTICLE_IDS = list(itertools.chain(range(1, 101), range(1051, 1101)))
	TEST_ARTICLE_IDS = list(itertools.chain(range(101, 151), range(501, 551), range(1101, 1151)))
	DEV_AND_TEST_IDS = DEV_ARTICLE_IDS + TEST_ARTICLE_IDS
	TRAIN_ARTICLE_IDS = [id_ for id_ in ALL_ARTICLE_IDS if id_ not in DEV_AND_TEST_IDS]


	class InquisitiveQgConfig(datasets.BuilderConfig):
	"""BuilderConfig for INQUISITIVE."""

	def __init__(self, **kwrags):
	"""BuilderConfig for INQUISITIVE.

	Args:
	**kwargs: keyword arguments forwarded to super.
	"""
	super(InquisitiveQgConfig, self).__init__(**kwrags)


	class InquisitiveQg(datasets.GeneratorBasedBuilder):
	"""Inquisitive Question Generation for High Level Text Comprehension"""

	VERSION = datasets.Version("1.0.0")
	BUILDER_CONFIGS = [
	InquisitiveQgConfig(name="plain_text", version=datasets.Version("1.0.0", ""), description="plain_text"),
	]

	def _info(self):
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=datasets.Features(
	{
	"id": datasets.Value("int32"),
	"article_id": datasets.Value("int32"),
	"article": datasets.Value("string"),
	"sentence_id": datasets.Value("int32"),
	"sentence": datasets.Value("string"),
	"span": datasets.Value("string"),
	"question": datasets.Value("string"),
	"span_start_position": datasets.Value("int32"),
	"span_end_position": datasets.Value("int32"),
	}
	),
	supervised_keys=None,
	homepage="https://github.com/wjko2/INQUISITIVE",
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	questions_file = dl_manager.download(_QUESTIONS_URL)
	archive = dl_manager.download(_ARTICLES_URL)
	articles_dir = "article"

	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	gen_kwargs={
	"articles_dir": articles_dir,
	"questions_file": questions_file,
	"article_ids": TRAIN_ARTICLE_IDS,
	"files": dl_manager.iter_archive(archive),
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.VALIDATION,
	gen_kwargs={
	"articles_dir": articles_dir,
	"questions_file": questions_file,
	"article_ids": DEV_ARTICLE_IDS,
	"files": dl_manager.iter_archive(archive),
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	gen_kwargs={
	"articles_dir": articles_dir,
	"questions_file": questions_file,
	"article_ids": TEST_ARTICLE_IDS,
	"files": dl_manager.iter_archive(archive),
	},
	),
	]

	def _generate_examples(self, articles_dir, questions_file, article_ids, files):
	articles = {}
	for path, f in files:
	articles[path] = f.read().decode("utf-8")
	with open(questions_file, encoding="utf-8") as f:
	questions_counter = 0
	rows = f.readlines()
	for i, row in enumerate(rows):
	if i == 0:
	continue # skip header line
	row = row.strip()
	cols = row.split("\t")

	article_id = int(cols[0])
	if article_id not in article_ids:
	continue

	fname = str(article_id).rjust(4, "0") + ".txt"
	article_path = articles_dir + "/" + fname
	article = articles[article_path]

	id_ = str(questions_counter)
	example = {
	"article_id": article_id,
	"sentence_id": int(cols[1]),
	"sentence": cols[2],
	"span": cols[3],
	"question": cols[4],
	"span_start_position": cols[5],
	"span_end_position": cols[6],
	"id": id_,
	"article": article,
	}
	yield id_, example
	questions_counter += 1