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| """ASSIN dataset.""" |
|
|
|
|
| import os |
| 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.""" |
| data_dir = dl_manager.download_and_extract(_URL) |
|
|
| train_paths = [] |
| dev_paths = [] |
| test_paths = [] |
|
|
| if self.config.name == "full" or self.config.name == "ptpt": |
| train_paths.append(os.path.join(data_dir, "assin-ptpt-train.xml")) |
| dev_paths.append(os.path.join(data_dir, "assin-ptpt-dev.xml")) |
| test_paths.append(os.path.join(data_dir, "assin-ptpt-test.xml")) |
|
|
| if self.config.name == "full" or self.config.name == "ptbr": |
| train_paths.append(os.path.join(data_dir, "assin-ptbr-train.xml")) |
| dev_paths.append(os.path.join(data_dir, "assin-ptbr-dev.xml")) |
| test_paths.append(os.path.join(data_dir, "assin-ptbr-test.xml")) |
|
|
| return [ |
| datasets.SplitGenerator( |
| name=datasets.Split.TRAIN, |
| gen_kwargs={ |
| "filepaths": train_paths, |
| "split": "train", |
| }, |
| ), |
| datasets.SplitGenerator( |
| name=datasets.Split.TEST, |
| gen_kwargs={ |
| "filepaths": test_paths, |
| "split": "test", |
| }, |
| ), |
| datasets.SplitGenerator( |
| name=datasets.Split.VALIDATION, |
| gen_kwargs={ |
| "filepaths": dev_paths, |
| "split": "dev", |
| }, |
| ), |
| ] |
|
|
| def _generate_examples(self, filepaths, split): |
| """Yields examples.""" |
|
|
| id_ = 0 |
|
|
| for filepath in filepaths: |
|
|
| with open(filepath, "rb") as f: |
|
|
| 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 |
|
|
