Datasets:
Tasks:
Text2Text Generation
Languages:
English
Multilinguality:
monolingual
Size Categories:
10K<n<100K
Language Creators:
crowdsourced
Annotations Creators:
crowdsourced
Source Datasets:
original
ArXiv:
License:
File size: 3,370 Bytes
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# coding=utf-8
# Copyright 2020 HuggingFace Datasets Authors.
#
# 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.
# Lint as: python3
"""E2E Dataset: New Challenges For End-to-End Generation, cleaned version"""
import csv
import datasets
_CITATION = """\
@inproceedings{dusek-etal-2019-semantic,
title = "Semantic Noise Matters for Neural Natural Language Generation",
author = "Du{\v{s}}ek, Ond{\v{r}}ej and
Howcroft, David M. and
Rieser, Verena",
booktitle = "Proceedings of the 12th International Conference on Natural Language Generation",
month = oct # "{--}" # nov,
year = "2019",
address = "Tokyo, Japan",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/W19-8652",
doi = "10.18653/v1/W19-8652",
pages = "421--426"
}
"""
_DESCRIPTION = """\
An update release of E2E NLG Challenge data with cleaned MRs and scripts, accompanying the following paper:
Ondřej Dušek, David M. Howcroft, and Verena Rieser (2019): Semantic Noise Matters for Neural Natural Language Generation. In INLG, Tokyo, Japan.
"""
_URL = "https://github.com/tuetschek/e2e-cleaning/raw/master/cleaned-data/"
_TRAINING_FILE = "train-fixed.no-ol.csv"
_DEV_FILE = "devel-fixed.no-ol.csv"
_TEST_FILE = "test-fixed.csv"
_URLS = {
"train": f"{_URL}{_TRAINING_FILE}",
"dev": f"{_URL}{_DEV_FILE}",
"test": f"{_URL}{_TEST_FILE}",
}
class E2eNLGCleaned(datasets.GeneratorBasedBuilder):
"""E2E dataset, cleaned version."""
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"meaning_representation": datasets.Value("string"),
"human_reference": datasets.Value("string"),
}
),
supervised_keys=None,
homepage="https://github.com/tuetschek/e2e-cleaning",
citation=_CITATION,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
downloaded_files = dl_manager.download_and_extract(_URLS)
return [
datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}),
datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_files["dev"]}),
datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["test"]}),
]
def _generate_examples(self, filepath):
with open(filepath, encoding="utf-8") as f:
reader = csv.DictReader(f)
for example_idx, example in enumerate(reader):
yield example_idx, {
"meaning_representation": example["mr"],
"human_reference": example["ref"],
}
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