Datasets:

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c3

	# 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.
	"""C3 Parallel Corpora"""


	import json

	import datasets


	_CITATION = """\
	@article{sun2019investigating,
	title={Investigating Prior Knowledge for Challenging Chinese Machine Reading Comprehension},
	author={Sun, Kai and Yu, Dian and Yu, Dong and Cardie, Claire},
	journal={Transactions of the Association for Computational Linguistics},
	year={2020},
	url={https://arxiv.org/abs/1904.09679v3}
	}
	"""

	_DESCRIPTION = """\
	Machine reading comprehension tasks require a machine reader to answer questions relevant to the given document. In this paper, we present the first free-form multiple-Choice Chinese machine reading Comprehension dataset (C^3), containing 13,369 documents (dialogues or more formally written mixed-genre texts) and their associated 19,577 multiple-choice free-form questions collected from Chinese-as-a-second-language examinations.
	We present a comprehensive analysis of the prior knowledge (i.e., linguistic, domain-specific, and general world knowledge) needed for these real-world problems. We implement rule-based and popular neural methods and find that there is still a significant performance gap between the best performing model (68.5%) and human readers (96.0%), especially on problems that require prior knowledge. We further study the effects of distractor plausibility and data augmentation based on translated relevant datasets for English on model performance. We expect C^3 to present great challenges to existing systems as answering 86.8% of questions requires both knowledge within and beyond the accompanying document, and we hope that C^3 can serve as a platform to study how to leverage various kinds of prior knowledge to better understand a given written or orally oriented text.
	"""

	_URL = "https://raw.githubusercontent.com/nlpdata/c3/master/data/"


	class C3Config(datasets.BuilderConfig):
	""" BuilderConfig for NewDataset"""

	def __init__(self, type_, **kwargs):
	"""

	Args:
	pair: the language pair to consider
	zip_file: The location of zip file containing original data
	**kwargs: keyword arguments forwarded to super.
	"""
	self.type_ = type_
	super().__init__(**kwargs)


	class C3(datasets.GeneratorBasedBuilder):
	"""C3 is the first free-form multiple-Choice Chinese machine reading Comprehension dataset, containing 13,369 documents (dialogues or more formally written mixed-genre texts) and their associated 19,577 multiple-choice free-form questions collected from Chinese-as-a-second language examinations."""

	VERSION = datasets.Version("1.0.0")

	# 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.
	BUILDER_CONFIG_CLASS = C3Config
	BUILDER_CONFIGS = [
	C3Config(
	name="mixed",
	description="Mixed genre questions",
	version=datasets.Version("1.0.0"),
	type_="mixed",
	),
	C3Config(
	name="dialog",
	description="Dialog questions",
	version=datasets.Version("1.0.0"),
	type_="dialog",
	),
	]

	def _info(self):
	return datasets.DatasetInfo(
	# This is the description that will appear on the datasets page.
	description=_DESCRIPTION,
	# datasets.features.FeatureConnectors
	features=datasets.Features(
	{
	"documents": datasets.Sequence(datasets.Value("string")),
	"document_id": datasets.Value("string"),
	"questions": datasets.Sequence(
	{
	"question": datasets.Value("string"),
	"answer": datasets.Value("string"),
	"choice": datasets.Sequence(datasets.Value("string")),
	}
	),
	}
	),
	# If there's a common (input, target) tuple from the features,
	# specify them here. They'll be used if as_supervised=True in
	# builder.as_dataset.
	supervised_keys=None,
	# Homepage of the dataset for documentation
	homepage="https://github.com/nlpdata/c3",
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	# m or d
	T = self.config.type_[0]
	files = [_URL + f"c3-{T}-{split}.json" for split in ["train", "test", "dev"]]
	dl_dir = dl_manager.download_and_extract(files)

	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"filename": dl_dir[0],
	"split": "train",
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"filename": dl_dir[1],
	"split": "test",
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.VALIDATION,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"filename": dl_dir[2],
	"split": "dev",
	},
	),
	]

	def _generate_examples(self, filename, split):
	""" Yields examples. """
	with open(filename, "r", encoding="utf-8") as sf:
	data = json.load(sf)
	for id_, (documents, questions, document_id) in enumerate(data):
	yield id_, {
	"documents": documents,
	"questions": questions,
	"document_id": document_id,
	}