Datasets:
Tasks:
Text Classification
Languages:
Portuguese
Multilinguality:
monolingual
Size Categories:
10K<n<100K
Language Creators:
found
Annotations Creators:
expert-generated
Source Datasets:
original
Tags:
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. | |
"""ASSIN dataset.""" | |
import xml.etree.ElementTree as ET | |
import datasets | |
_CITATION = """ | |
@inproceedings{fonseca2016assin, | |
title={ASSIN: Avaliacao de similaridade semantica e inferencia textual}, | |
author={Fonseca, E and Santos, L and Criscuolo, Marcelo and Aluisio, S}, | |
booktitle={Computational Processing of the Portuguese Language-12th International Conference, Tomar, Portugal}, | |
pages={13--15}, | |
year={2016} | |
} | |
""" | |
_DESCRIPTION = """ | |
The ASSIN (Avaliação de Similaridade Semântica e INferência textual) corpus is a corpus annotated with pairs of sentences written in | |
Portuguese that is suitable for the exploration of textual entailment and paraphrasing classifiers. The corpus contains pairs of sentences | |
extracted from news articles written in European Portuguese (EP) and Brazilian Portuguese (BP), obtained from Google News Portugal | |
and Brazil, respectively. To create the corpus, the authors started by collecting a set of news articles describing the | |
same event (one news article from Google News Portugal and another from Google News Brazil) from Google News. | |
Then, they employed Latent Dirichlet Allocation (LDA) models to retrieve pairs of similar sentences between sets of news | |
articles that were grouped together around the same topic. For that, two LDA models were trained (for EP and for BP) | |
on external and large-scale collections of unannotated news articles from Portuguese and Brazilian news providers, respectively. | |
Then, the authors defined a lower and upper threshold for the sentence similarity score of the retrieved pairs of sentences, | |
taking into account that high similarity scores correspond to sentences that contain almost the same content (paraphrase candidates), | |
and low similarity scores correspond to sentences that are very different in content from each other (no-relation candidates). | |
From the collection of pairs of sentences obtained at this stage, the authors performed some manual grammatical corrections | |
and discarded some of the pairs wrongly retrieved. Furthermore, from a preliminary analysis made to the retrieved sentence pairs | |
the authors noticed that the number of contradictions retrieved during the previous stage was very low. Additionally, they also | |
noticed that event though paraphrases are not very frequent, they occur with some frequency in news articles. Consequently, | |
in contrast with the majority of the currently available corpora for other languages, which consider as labels “neutral”, “entailment” | |
and “contradiction” for the task of RTE, the authors of the ASSIN corpus decided to use as labels “none”, “entailment” and “paraphrase”. | |
Finally, the manual annotation of pairs of sentences was performed by human annotators. At least four annotators were randomly | |
selected to annotate each pair of sentences, which is done in two steps: (i) assigning a semantic similarity label (a score between 1 and 5, | |
from unrelated to very similar); and (ii) providing an entailment label (one sentence entails the other, sentences are paraphrases, | |
or no relation). Sentence pairs where at least three annotators do not agree on the entailment label were considered controversial | |
and thus discarded from the gold standard annotations. The full dataset has 10,000 sentence pairs, half of which in Brazilian Portuguese | |
and half in European Portuguese. Either language variant has 2,500 pairs for training, 500 for validation and 2,000 for testing. | |
""" | |
_HOMEPAGE = "http://nilc.icmc.usp.br/assin/" | |
_LICENSE = "" | |
_URL = "http://nilc.icmc.usp.br/assin/assin.tar.gz" | |
class Assin(datasets.GeneratorBasedBuilder): | |
"""ASSIN dataset.""" | |
VERSION = datasets.Version("1.0.0") | |
BUILDER_CONFIGS = [ | |
datasets.BuilderConfig( | |
name="full", | |
version=VERSION, | |
description="If you want to use all the ASSIN data (Brazilian Portuguese and European Portuguese)", | |
), | |
datasets.BuilderConfig( | |
name="ptpt", | |
version=VERSION, | |
description="If you want to use only the ASSIN European Portuguese subset", | |
), | |
datasets.BuilderConfig( | |
name="ptbr", | |
version=VERSION, | |
description="If you want to use only the ASSIN Brazilian Portuguese subset", | |
), | |
] | |
DEFAULT_CONFIG_NAME = "full" | |
def _info(self): | |
features = datasets.Features( | |
{ | |
"sentence_pair_id": datasets.Value("int64"), | |
"premise": datasets.Value("string"), | |
"hypothesis": datasets.Value("string"), | |
"relatedness_score": datasets.Value("float32"), | |
"entailment_judgment": datasets.features.ClassLabel(names=["NONE", "ENTAILMENT", "PARAPHRASE"]), | |
} | |
) | |
return datasets.DatasetInfo( | |
description=_DESCRIPTION, | |
features=features, | |
supervised_keys=None, | |
homepage=_HOMEPAGE, | |
license=_LICENSE, | |
citation=_CITATION, | |
) | |
def _split_generators(self, dl_manager): | |
"""Returns SplitGenerators.""" | |
archive = dl_manager.download(_URL) | |
train_paths = [] | |
dev_paths = [] | |
test_paths = [] | |
if self.config.name == "full" or self.config.name == "ptpt": | |
train_paths.append("assin-ptpt-train.xml") | |
dev_paths.append("assin-ptpt-dev.xml") | |
test_paths.append("assin-ptpt-test.xml") | |
if self.config.name == "full" or self.config.name == "ptbr": | |
train_paths.append("assin-ptbr-train.xml") | |
dev_paths.append("assin-ptbr-dev.xml") | |
test_paths.append("assin-ptbr-test.xml") | |
return [ | |
datasets.SplitGenerator( | |
name=datasets.Split.TRAIN, | |
gen_kwargs={ | |
"filepaths": train_paths, | |
"files": dl_manager.iter_archive(archive), | |
}, | |
), | |
datasets.SplitGenerator( | |
name=datasets.Split.TEST, | |
gen_kwargs={ | |
"filepaths": test_paths, | |
"files": dl_manager.iter_archive(archive), | |
}, | |
), | |
datasets.SplitGenerator( | |
name=datasets.Split.VALIDATION, | |
gen_kwargs={ | |
"filepaths": dev_paths, | |
"files": dl_manager.iter_archive(archive), | |
}, | |
), | |
] | |
def _generate_examples(self, filepaths, files): | |
"""Yields examples.""" | |
id_ = 0 | |
for path, f in files: | |
if path in filepaths: | |
tree = ET.parse(f) | |
root = tree.getroot() | |
for pair in root: | |
yield id_, { | |
"sentence_pair_id": int(pair.attrib.get("id")), | |
"premise": pair.find(".//t").text, | |
"hypothesis": pair.find(".//h").text, | |
"relatedness_score": float(pair.attrib.get("similarity")), | |
"entailment_judgment": pair.attrib.get("entailment").upper(), | |
} | |
id_ += 1 | |