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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.
"""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_]}
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