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