Datasets:

jhu-clsp
/

jfleg

	# 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.
	"""JFLEG dataset."""


	import datasets


	_CITATION = """\
	@InProceedings{napoles-sakaguchi-tetreault:2017:EACLshort,
	author = {Napoles, Courtney
	and Sakaguchi, Keisuke
	and Tetreault, Joel},
	title = {JFLEG: A Fluency Corpus and Benchmark for Grammatical Error Correction},
	booktitle = {Proceedings of the 15th Conference of the European Chapter of the
	Association for Computational Linguistics: Volume 2, Short Papers},
	month = {April},
	year = {2017},
	address = {Valencia, Spain},
	publisher = {Association for Computational Linguistics},
	pages = {229--234},
	url = {http://www.aclweb.org/anthology/E17-2037}
	}
	@InProceedings{heilman-EtAl:2014:P14-2,
	author = {Heilman, Michael
	and Cahill, Aoife
	and Madnani, Nitin
	and Lopez, Melissa
	and Mulholland, Matthew
	and Tetreault, Joel},
	title = {Predicting Grammaticality on an Ordinal Scale},
	booktitle = {Proceedings of the 52nd Annual Meeting of the
	Association for Computational Linguistics (Volume 2: Short Papers)},
	month = {June},
	year = {2014},
	address = {Baltimore, Maryland},
	publisher = {Association for Computational Linguistics},
	pages = {174--180},
	url = {http://www.aclweb.org/anthology/P14-2029}
	}
	"""

	_DESCRIPTION = """\
	JFLEG (JHU FLuency-Extended GUG) is an English grammatical error correction (GEC) corpus.
	It is a gold standard benchmark for developing and evaluating GEC systems with respect to
	fluency (extent to which a text is native-sounding) as well as grammaticality.

	For each source document, there are four human-written corrections (ref0 to ref3).
	"""

	_HOMEPAGE = "https://github.com/keisks/jfleg"

	_LICENSE = "CC BY-NC-SA 4.0"

	_URLs = {
	"dev": {
	"src": "https://raw.githubusercontent.com/keisks/jfleg/master/dev/dev.src",
	"ref0": "https://raw.githubusercontent.com/keisks/jfleg/master/dev/dev.ref0",
	"ref1": "https://raw.githubusercontent.com/keisks/jfleg/master/dev/dev.ref1",
	"ref2": "https://raw.githubusercontent.com/keisks/jfleg/master/dev/dev.ref2",
	"ref3": "https://raw.githubusercontent.com/keisks/jfleg/master/dev/dev.ref3",
	},
	"test": {
	"src": "https://raw.githubusercontent.com/keisks/jfleg/master/test/test.src",
	"ref0": "https://raw.githubusercontent.com/keisks/jfleg/master/test/test.ref0",
	"ref1": "https://raw.githubusercontent.com/keisks/jfleg/master/test/test.ref1",
	"ref2": "https://raw.githubusercontent.com/keisks/jfleg/master/test/test.ref2",
	"ref3": "https://raw.githubusercontent.com/keisks/jfleg/master/test/test.ref3",
	},
	}


	class Jfleg(datasets.GeneratorBasedBuilder):
	"""JFLEG (JHU FLuency-Extended GUG) grammatical error correction dataset."""

	VERSION = datasets.Version("1.0.0")

	def _info(self):
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=datasets.Features(
	{"sentence": datasets.Value("string"), "corrections": datasets.Sequence(datasets.Value("string"))}
	),
	supervised_keys=None,
	homepage=_HOMEPAGE,
	license=_LICENSE,
	citation=_CITATION,
	)

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

	downloaded_dev = dl_manager.download_and_extract(_URLs["dev"])
	downloaded_test = dl_manager.download_and_extract(_URLs["test"])

	return [
	datasets.SplitGenerator(
	name=datasets.Split.VALIDATION,
	gen_kwargs={
	"filepath": downloaded_dev,
	"split": "dev",
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	gen_kwargs={"filepath": downloaded_test, "split": "test"},
	),
	]

	def _generate_examples(self, filepath, split):
	"""Yields examples."""

	source_file = filepath["src"]
	with open(source_file, encoding="utf-8") as f:
	source_sentences = f.read().split("\n")
	num_source = len(source_sentences)

	corrections = []
	for n in range(0, 4):
	correction_file = filepath["ref{n}".format(n=n)]
	with open(correction_file, encoding="utf-8") as f:
	correction_sentences = f.read().split("\n")
	num_correction = len(correction_sentences)

	assert len(correction_sentences) == len(
	source_sentences
	), "Sizes do not match: {ns} vs {nr} for {sf} vs {cf}.".format(
	ns=num_source, nr=num_correction, sf=source_file, cf=correction_file
	)
	corrections.append(correction_sentences)

	corrected_sentences = list(zip(*corrections))
	for id_, source_sentence in enumerate(source_sentences):
	yield id_, {"sentence": source_sentence, "corrections": corrected_sentences[id_]}