Datasets:
Tasks:
Text2Text Generation
Sub-tasks:
text-simplification
Languages:
English
Multilinguality:
monolingual
Size Categories:
100K<n<1M
Language Creators:
found
Source Datasets:
extended|other-wikipedia
ArXiv:
Tags:
License:
File size: 13,227 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.
"""WikiAuto dataset for Text Simplification"""
import json
import datasets
_CITATION = """\
@inproceedings{acl/JiangMLZX20,
author = {Chao Jiang and
Mounica Maddela and
Wuwei Lan and
Yang Zhong and
Wei Xu},
editor = {Dan Jurafsky and
Joyce Chai and
Natalie Schluter and
Joel R. Tetreault},
title = {Neural {CRF} Model for Sentence Alignment in Text Simplification},
booktitle = {Proceedings of the 58th Annual Meeting of the Association for Computational
Linguistics, {ACL} 2020, Online, July 5-10, 2020},
pages = {7943--7960},
publisher = {Association for Computational Linguistics},
year = {2020},
url = {https://www.aclweb.org/anthology/2020.acl-main.709/}
}
"""
# TODO: Add description of the dataset here
# You can copy an official description
_DESCRIPTION = """\
WikiAuto provides a set of aligned sentences from English Wikipedia and Simple English Wikipedia
as a resource to train sentence simplification systems. The authors first crowd-sourced a set of manual alignments
between sentences in a subset of the Simple English Wikipedia and their corresponding versions in English Wikipedia
(this corresponds to the `manual` config), then trained a neural CRF system to predict these alignments.
The trained model was then applied to the other articles in Simple English Wikipedia with an English counterpart to
create a larger corpus of aligned sentences (corresponding to the `auto`, `auto_acl`, `auto_full_no_split`, and `auto_full_with_split` configs here).
"""
# TODO: Add the licence for the dataset here if you can find it
_LICENSE = "CC-BY-SA 3.0"
# TODO: Add link to the official dataset URLs here
# The HuggingFace dataset library don't host the datasets but only point to the original files
# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
_URLs = {
"manual": {
"train": "https://www.dropbox.com/sh/ohqaw41v48c7e5p/AACdl4UPKtu7CMMa-CJhz4G7a/wiki-manual/train.tsv?dl=1",
"dev": "https://github.com/chaojiang06/wiki-auto/raw/master/wiki-manual/dev.tsv",
"test": "https://github.com/chaojiang06/wiki-auto/raw/master/wiki-manual/test.tsv",
},
"auto_acl": {
"normal": "https://github.com/chaojiang06/wiki-auto/raw/master/wiki-auto/ACL2020/train.src",
"simple": "https://github.com/chaojiang06/wiki-auto/raw/master/wiki-auto/ACL2020/train.dst",
},
"auto_full_no_split": {
"normal": "https://github.com/chaojiang06/wiki-auto/raw/master/wiki-auto/GEM2021/full_no_split/train.src",
"simple": "https://github.com/chaojiang06/wiki-auto/raw/master/wiki-auto/GEM2021/full_no_split/train.dst",
},
"auto_full_with_split": {
"normal": "https://github.com/chaojiang06/wiki-auto/raw/master/wiki-auto/GEM2021/full_with_split/train.src",
"simple": "https://github.com/chaojiang06/wiki-auto/raw/master/wiki-auto/GEM2021/full_with_split/train.dst",
},
"auto": {
"part_1": "https://www.dropbox.com/sh/ohqaw41v48c7e5p/AAATBDhU1zpdcT5x5WgO8DMaa/wiki-auto-all-data/wiki-auto-part-1-data.json?dl=1",
"part_2": "https://www.dropbox.com/sh/ohqaw41v48c7e5p/AAATgPkjo_tPt9z12vZxJ3MRa/wiki-auto-all-data/wiki-auto-part-2-data.json?dl=1",
},
}
# TODO: Name of the dataset usually match the script name with CamelCase instead of snake_case
class WikiAuto(datasets.GeneratorBasedBuilder):
"""WikiAuto dataset for sentence simplification"""
VERSION = datasets.Version("1.0.0")
BUILDER_CONFIGS = [
datasets.BuilderConfig(
name="manual",
version=VERSION,
description="A set of 10K Wikipedia sentence pairs aligned by crowd workers.",
),
datasets.BuilderConfig(
name="auto_acl",
version=VERSION,
description="Automatically aligned and filtered sentence pairs used to train the ACL2020 system.",
),
datasets.BuilderConfig(
name="auto_full_no_split",
version=VERSION,
description="All automatically aligned sentence pairs without sentence splitting.",
),
datasets.BuilderConfig(
name="auto_full_with_split",
version=VERSION,
description="All automatically aligned sentence pairs with sentence splitting.",
),
datasets.BuilderConfig(
name="auto", version=VERSION, description="A large set of automatically aligned sentence pairs."
),
]
DEFAULT_CONFIG_NAME = "auto"
def _info(self):
if self.config.name == "manual": # This is the name of the configuration selected in BUILDER_CONFIGS above
features = datasets.Features(
{
"alignment_label": datasets.ClassLabel(names=["notAligned", "aligned", "partialAligned"]),
"normal_sentence_id": datasets.Value("string"),
"simple_sentence_id": datasets.Value("string"),
"normal_sentence": datasets.Value("string"),
"simple_sentence": datasets.Value("string"),
"gleu_score": datasets.Value("float32"),
}
)
elif (
self.config.name == "auto_acl"
or self.config.name == "auto_full_no_split"
or self.config.name == "auto_full_with_split"
):
features = datasets.Features(
{
"normal_sentence": datasets.Value("string"),
"simple_sentence": datasets.Value("string"),
}
)
else:
features = datasets.Features(
{
"example_id": datasets.Value("string"),
"normal": {
"normal_article_id": datasets.Value("int32"),
"normal_article_title": datasets.Value("string"),
"normal_article_url": datasets.Value("string"),
"normal_article_content": datasets.Sequence(
{
"normal_sentence_id": datasets.Value("string"),
"normal_sentence": datasets.Value("string"),
}
),
},
"simple": {
"simple_article_id": datasets.Value("int32"),
"simple_article_title": datasets.Value("string"),
"simple_article_url": datasets.Value("string"),
"simple_article_content": datasets.Sequence(
{
"simple_sentence_id": datasets.Value("string"),
"simple_sentence": datasets.Value("string"),
}
),
},
"paragraph_alignment": datasets.Sequence(
{
"normal_paragraph_id": datasets.Value("string"),
"simple_paragraph_id": datasets.Value("string"),
}
),
"sentence_alignment": datasets.Sequence(
{
"normal_sentence_id": datasets.Value("string"),
"simple_sentence_id": datasets.Value("string"),
}
),
}
)
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
supervised_keys=None,
homepage="https://github.com/chaojiang06/wiki-auto",
license=_LICENSE,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
my_urls = _URLs[self.config.name]
data_dir = dl_manager.download_and_extract(my_urls)
if self.config.name in ["manual", "auto"]:
return [
datasets.SplitGenerator(
name=spl,
gen_kwargs={
"filepaths": data_dir,
"split": spl,
},
)
for spl in data_dir
]
else:
return [
datasets.SplitGenerator(
name="full",
gen_kwargs={"filepaths": data_dir, "split": "full"},
)
]
def _generate_examples(self, filepaths, split):
if self.config.name == "manual":
keys = [
"alignment_label",
"simple_sentence_id",
"normal_sentence_id",
"simple_sentence",
"normal_sentence",
"gleu_score",
]
with open(filepaths[split], encoding="utf-8") as f:
for id_, line in enumerate(f):
values = line.strip().split("\t")
assert len(values) == 6, f"Not enough fields in ---- {line} --- {values}"
yield id_, dict(
[(k, val) if k != "gleu_score" else (k, float(val)) for k, val in zip(keys, values)]
)
elif (
self.config.name == "auto_acl"
or self.config.name == "auto_full_no_split"
or self.config.name == "auto_full_with_split"
):
with open(filepaths["normal"], encoding="utf-8") as fi:
with open(filepaths["simple"], encoding="utf-8") as fo:
for id_, (norm_se, simp_se) in enumerate(zip(fi, fo)):
yield id_, {
"normal_sentence": norm_se,
"simple_sentence": simp_se,
}
else:
dataset_dict = json.load(open(filepaths[split], encoding="utf-8"))
for id_, (eid, example_dict) in enumerate(dataset_dict.items()):
res = {
"example_id": eid,
"normal": {
"normal_article_id": example_dict["normal"]["id"],
"normal_article_title": example_dict["normal"]["title"],
"normal_article_url": example_dict["normal"]["url"],
"normal_article_content": {
"normal_sentence_id": [
sen_id for sen_id, sen_txt in example_dict["normal"]["content"].items()
],
"normal_sentence": [
sen_txt for sen_id, sen_txt in example_dict["normal"]["content"].items()
],
},
},
"simple": {
"simple_article_id": example_dict["simple"]["id"],
"simple_article_title": example_dict["simple"]["title"],
"simple_article_url": example_dict["simple"]["url"],
"simple_article_content": {
"simple_sentence_id": [
sen_id for sen_id, sen_txt in example_dict["simple"]["content"].items()
],
"simple_sentence": [
sen_txt for sen_id, sen_txt in example_dict["simple"]["content"].items()
],
},
},
"paragraph_alignment": {
"normal_paragraph_id": [
norm_id for simp_id, norm_id in example_dict.get("paragraph_alignment", [])
],
"simple_paragraph_id": [
simp_id for simp_id, norm_id in example_dict.get("paragraph_alignment", [])
],
},
"sentence_alignment": {
"normal_sentence_id": [
norm_id for simp_id, norm_id in example_dict.get("sentence_alignment", [])
],
"simple_sentence_id": [
simp_id for simp_id, norm_id in example_dict.get("sentence_alignment", [])
],
},
}
yield id_, res
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