Datasets:
kor_sae

	# 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.
	"""Structured Argument Extraction for Korean"""


	import csv

	import datasets


	_CITATION = """\
	@article{cho2019machines,
	title={Machines Getting with the Program: Understanding Intent Arguments of Non-Canonical Directives},
	author={Cho, Won Ik and Moon, Young Ki and Moon, Sangwhan and Kim, Seok Min and Kim, Nam Soo},
	journal={arXiv preprint arXiv:1912.00342},
	year={2019}
	}
	"""

	_DESCRIPTION = """\
	This new dataset is designed to extract intent from non-canonical directives which will help dialog managers
	extract intent from user dialog that may have no clear objective or are paraphrased forms of utterances.
	"""

	_HOMEPAGE = "https://github.com/warnikchow/sae4k"

	_LICENSE = "CC-BY-SA-4.0"

	_TRAIN_DOWNLOAD_URL = "https://raw.githubusercontent.com/warnikchow/sae4k/master/data/sae4k_v1.txt"


	class KorSae(datasets.GeneratorBasedBuilder):
	"""Structured Argument Extraction for Korean"""

	VERSION = datasets.Version("1.1.0")

	def _info(self):

	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=datasets.Features(
	{
	"intent_pair1": datasets.Value("string"),
	"intent_pair2": datasets.Value("string"),
	"label": datasets.features.ClassLabel(
	names=[
	"yes/no",
	"alternative",
	"wh- questions",
	"prohibitions",
	"requirements",
	"strong requirements",
	]
	),
	}
	),
	supervised_keys=None,
	homepage=_HOMEPAGE,
	license=_LICENSE,
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	"""Returns SplitGenerators."""

	train_path = dl_manager.download_and_extract(_TRAIN_DOWNLOAD_URL)
	return [
	datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": train_path}),
	]

	def _generate_examples(self, filepath):
	"""Generate KorSAE examples"""

	with open(filepath, encoding="utf-8") as csv_file:
	data = csv.reader(csv_file, delimiter="\t")
	for id_, row in enumerate(data):
	intent_pair1, intent_pair2, label = row
	yield id_, {"intent_pair1": intent_pair1, "intent_pair2": intent_pair2, "label": int(label)}