Datasets:
Sub-tasks:
text-simplification
Languages:
English
Multilinguality:
monolingual
Size Categories:
1K<n<10K
Language Creators:
found
Annotations Creators:
machine-generated
License:
# 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. | |
"""ASSET: a dataset for sentence simplification evaluation""" | |
import csv | |
import datasets | |
_CITATION = """\ | |
@inproceedings{alva-manchego-etal-2020-asset, | |
title = "{ASSET}: {A} Dataset for Tuning and Evaluation of Sentence Simplification Models with Multiple Rewriting Transformations", | |
author = "Alva-Manchego, Fernando and | |
Martin, Louis and | |
Bordes, Antoine and | |
Scarton, Carolina and | |
Sagot, Benoit and | |
Specia, Lucia", | |
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics", | |
month = jul, | |
year = "2020", | |
address = "Online", | |
publisher = "Association for Computational Linguistics", | |
url = "https://www.aclweb.org/anthology/2020.acl-main.424", | |
pages = "4668--4679", | |
} | |
""" | |
_DESCRIPTION = """\ | |
ASSET is a dataset for evaluating Sentence Simplification systems with multiple rewriting transformations, | |
as described in "ASSET: A Dataset for Tuning and Evaluation of Sentence Simplification Models with Multiple Rewriting Transformations". | |
The corpus is composed of 2000 validation and 359 test original sentences that were each simplified 10 times by different annotators. | |
The corpus also contains human judgments of meaning preservation, fluency and simplicity for the outputs of several automatic text simplification systems. | |
""" | |
_HOMEPAGE = "https://github.com/facebookresearch/asset" | |
_LICENSE = "Creative Common Attribution-NonCommercial 4.0 International" | |
_URL_LIST = [ | |
("human_ratings.csv", "https://github.com/facebookresearch/asset/raw/main/human_ratings/human_ratings.csv"), | |
("asset.valid.orig", "https://github.com/facebookresearch/asset/raw/main/dataset/asset.valid.orig"), | |
("asset.test.orig", "https://github.com/facebookresearch/asset/raw/main/dataset/asset.test.orig"), | |
] | |
_URL_LIST += [ | |
( | |
f"asset.{spl}.simp.{i}", | |
f"https://github.com/facebookresearch/asset/raw/main/dataset/asset.{spl}.simp.{i}", | |
) | |
for spl in ["valid", "test"] | |
for i in range(10) | |
] | |
_URLs = dict(_URL_LIST) | |
class Asset(datasets.GeneratorBasedBuilder): | |
VERSION = datasets.Version("1.0.0") | |
BUILDER_CONFIGS = [ | |
datasets.BuilderConfig( | |
name="simplification", | |
version=VERSION, | |
description="A set of original sentences aligned with 10 possible simplifications for each.", | |
), | |
datasets.BuilderConfig( | |
name="ratings", version=VERSION, description="Human ratings of automatically produced text implification." | |
), | |
] | |
DEFAULT_CONFIG_NAME = "simplification" | |
def _info(self): | |
if self.config.name == "simplification": | |
features = datasets.Features( | |
{ | |
"original": datasets.Value("string"), | |
"simplifications": datasets.Sequence(datasets.Value("string")), | |
} | |
) | |
else: | |
features = datasets.Features( | |
{ | |
"original": datasets.Value("string"), | |
"simplification": datasets.Value("string"), | |
"original_sentence_id": datasets.Value("int32"), | |
"aspect": datasets.ClassLabel(names=["meaning", "fluency", "simplicity"]), | |
"worker_id": datasets.Value("int32"), | |
"rating": datasets.Value("int32"), | |
} | |
) | |
return datasets.DatasetInfo( | |
description=_DESCRIPTION, | |
features=features, | |
supervised_keys=None, | |
homepage=_HOMEPAGE, | |
license=_LICENSE, | |
citation=_CITATION, | |
) | |
def _split_generators(self, dl_manager): | |
data_dir = dl_manager.download_and_extract(_URLs) | |
if self.config.name == "simplification": | |
return [ | |
datasets.SplitGenerator( | |
name=datasets.Split.VALIDATION, | |
gen_kwargs={ | |
"filepaths": data_dir, | |
"split": "valid", | |
}, | |
), | |
datasets.SplitGenerator( | |
name=datasets.Split.TEST, | |
gen_kwargs={"filepaths": data_dir, "split": "test"}, | |
), | |
] | |
else: | |
return [ | |
datasets.SplitGenerator( | |
name="full", | |
gen_kwargs={ | |
"filepaths": data_dir, | |
"split": "full", | |
}, | |
), | |
] | |
def _generate_examples(self, filepaths, split): | |
"""Yields examples.""" | |
if self.config.name == "simplification": | |
files = [open(filepaths[f"asset.{split}.orig"], encoding="utf-8")] + [ | |
open(filepaths[f"asset.{split}.simp.{i}"], encoding="utf-8") for i in range(10) | |
] | |
for id_, lines in enumerate(zip(*files)): | |
yield id_, {"original": lines[0].strip(), "simplifications": [line.strip() for line in lines[1:]]} | |
else: | |
with open(filepaths["human_ratings.csv"], encoding="utf-8") as f: | |
reader = csv.reader(f, delimiter=",") | |
for id_, row in enumerate(reader): | |
if id_ == 0: | |
keys = row[:] | |
else: | |
res = dict([(k, v) for k, v in zip(keys, row)]) | |
for k in ["original_sentence_id", "worker_id", "rating"]: | |
res[k] = int(res[k]) | |
yield (id_ - 1), res | |