Datasets:
Tasks:
Text Classification
Sub-tasks:
intent-classification
Languages:
Korean
Size:
10K<n<100K
ArXiv:
License:
File size: 3,158 Bytes
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# 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)}
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