| | --- |
| | annotations_creators: |
| | - expert-generated |
| | language_creators: |
| | - found |
| | language: |
| | - pt |
| | license: |
| | - unknown |
| | multilinguality: |
| | - monolingual |
| | size_categories: |
| | - 10K<n<100K |
| | source_datasets: |
| | - original |
| | task_categories: |
| | - text-classification |
| | task_ids: |
| | - text-scoring |
| | - natural-language-inference |
| | - semantic-similarity-scoring |
| | paperswithcode_id: assin |
| | pretty_name: ASSIN |
| | dataset_info: |
| | - config_name: full |
| | features: |
| | - name: sentence_pair_id |
| | dtype: int64 |
| | - name: premise |
| | dtype: string |
| | - name: hypothesis |
| | dtype: string |
| | - name: relatedness_score |
| | dtype: float32 |
| | - name: entailment_judgment |
| | dtype: |
| | class_label: |
| | names: |
| | '0': NONE |
| | '1': ENTAILMENT |
| | '2': PARAPHRASE |
| | splits: |
| | - name: train |
| | num_bytes: 986499 |
| | num_examples: 5000 |
| | - name: test |
| | num_bytes: 767304 |
| | num_examples: 4000 |
| | - name: validation |
| | num_bytes: 196821 |
| | num_examples: 1000 |
| | download_size: 1335013 |
| | dataset_size: 1950624 |
| | - config_name: ptbr |
| | features: |
| | - name: sentence_pair_id |
| | dtype: int64 |
| | - name: premise |
| | dtype: string |
| | - name: hypothesis |
| | dtype: string |
| | - name: relatedness_score |
| | dtype: float32 |
| | - name: entailment_judgment |
| | dtype: |
| | class_label: |
| | names: |
| | '0': NONE |
| | '1': ENTAILMENT |
| | '2': PARAPHRASE |
| | splits: |
| | - name: train |
| | num_bytes: 463505 |
| | num_examples: 2500 |
| | - name: test |
| | num_bytes: 374424 |
| | num_examples: 2000 |
| | - name: validation |
| | num_bytes: 91203 |
| | num_examples: 500 |
| | download_size: 639490 |
| | dataset_size: 929132 |
| | - config_name: ptpt |
| | features: |
| | - name: sentence_pair_id |
| | dtype: int64 |
| | - name: premise |
| | dtype: string |
| | - name: hypothesis |
| | dtype: string |
| | - name: relatedness_score |
| | dtype: float32 |
| | - name: entailment_judgment |
| | dtype: |
| | class_label: |
| | names: |
| | '0': NONE |
| | '1': ENTAILMENT |
| | '2': PARAPHRASE |
| | splits: |
| | - name: train |
| | num_bytes: 522994 |
| | num_examples: 2500 |
| | - name: test |
| | num_bytes: 392880 |
| | num_examples: 2000 |
| | - name: validation |
| | num_bytes: 105618 |
| | num_examples: 500 |
| | download_size: 706661 |
| | dataset_size: 1021492 |
| | configs: |
| | - config_name: full |
| | data_files: |
| | - split: train |
| | path: full/train-* |
| | - split: test |
| | path: full/test-* |
| | - split: validation |
| | path: full/validation-* |
| | default: true |
| | - config_name: ptbr |
| | data_files: |
| | - split: train |
| | path: ptbr/train-* |
| | - split: test |
| | path: ptbr/test-* |
| | - split: validation |
| | path: ptbr/validation-* |
| | - config_name: ptpt |
| | data_files: |
| | - split: train |
| | path: ptpt/train-* |
| | - split: test |
| | path: ptpt/test-* |
| | - split: validation |
| | path: ptpt/validation-* |
| | --- |
| | |
| | # Dataset Card for ASSIN |
| |
|
| | ## Table of Contents |
| | - [Dataset Description](#dataset-description) |
| | - [Dataset Summary](#dataset-summary) |
| | - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) |
| | - [Languages](#languages) |
| | - [Dataset Structure](#dataset-structure) |
| | - [Data Instances](#data-instances) |
| | - [Data Fields](#data-fields) |
| | - [Data Splits](#data-splits) |
| | - [Dataset Creation](#dataset-creation) |
| | - [Curation Rationale](#curation-rationale) |
| | - [Source Data](#source-data) |
| | - [Annotations](#annotations) |
| | - [Personal and Sensitive Information](#personal-and-sensitive-information) |
| | - [Considerations for Using the Data](#considerations-for-using-the-data) |
| | - [Social Impact of Dataset](#social-impact-of-dataset) |
| | - [Discussion of Biases](#discussion-of-biases) |
| | - [Other Known Limitations](#other-known-limitations) |
| | - [Additional Information](#additional-information) |
| | - [Dataset Curators](#dataset-curators) |
| | - [Licensing Information](#licensing-information) |
| | - [Citation Information](#citation-information) |
| | - [Contributions](#contributions) |
| |
|
| | ## Dataset Description |
| |
|
| | - **Homepage:** [ASSIN homepage](http://nilc.icmc.usp.br/assin/) |
| | - **Repository:** [ASSIN repository](http://nilc.icmc.usp.br/assin/) |
| | - **Paper:** [ASSIN: Evaluation of Semantic Similarity and Textual Inference](http://propor2016.di.fc.ul.pt/wp-content/uploads/2015/10/assin-overview.pdf) |
| | - **Point of Contact:** [Erick Rocha Fonseca](mailto:erickrf@icmc.usp.br) |
| |
|
| | ### Dataset Summary |
| |
|
| | 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 (ptbr) |
| | and half in European Portuguese (ptpt). Either language variant has 2,500 pairs for training, 500 for validation and 2,000 for testing. |
| |
|
| | ### Supported Tasks and Leaderboards |
| |
|
| | [More Information Needed] |
| |
|
| | ### Languages |
| |
|
| | The language supported is Portuguese. |
| |
|
| | ## Dataset Structure |
| |
|
| | ### Data Instances |
| |
|
| | An example from the ASSIN dataset looks as follows: |
| |
|
| | ``` |
| | { |
| | "entailment_judgment": 0, |
| | "hypothesis": "André Gomes entra em campo quatro meses depois de uma lesão na perna esquerda o ter afastado dos relvados.", |
| | "premise": "Relembre-se que o atleta estava afastado dos relvados desde maio, altura em que contraiu uma lesão na perna esquerda.", |
| | "relatedness_score": 3.5, |
| | "sentence_pair_id": 1 |
| | } |
| | ``` |
| |
|
| | ### Data Fields |
| |
|
| | - `sentence_pair_id`: a `int64` feature. |
| | - `premise`: a `string` feature. |
| | - `hypothesis`: a `string` feature. |
| | - `relatedness_score`: a `float32` feature. |
| | - `entailment_judgment`: a classification label, with possible values including `NONE`, `ENTAILMENT`, `PARAPHRASE`. |
| |
|
| | ### Data Splits |
| |
|
| | The data is split into train, validation and test set. The split sizes are as follow: |
| |
|
| | | | Train | Val | Test | |
| | | ----- | ------ | ----- | ---- | |
| | | full | 5000 | 1000 | 4000 | |
| | | ptbr | 2500 | 500 | 2000 | |
| | | ptpt | 2500 | 500 | 2000 | |
| |
|
| | ## Dataset Creation |
| |
|
| | ### Curation Rationale |
| |
|
| | [More Information Needed] |
| |
|
| | ### Source Data |
| |
|
| | #### Initial Data Collection and Normalization |
| |
|
| | [More Information Needed] |
| |
|
| | #### Who are the source language producers? |
| |
|
| | [More Information Needed] |
| |
|
| | ### Annotations |
| |
|
| | #### Annotation process |
| |
|
| | [More Information Needed] |
| |
|
| | #### Who are the annotators? |
| |
|
| | [More Information Needed] |
| |
|
| | ### Personal and Sensitive Information |
| |
|
| | [More Information Needed] |
| |
|
| | ## Considerations for Using the Data |
| |
|
| | ### Social Impact of Dataset |
| |
|
| | [More Information Needed] |
| |
|
| | ### Discussion of Biases |
| |
|
| | [More Information Needed] |
| |
|
| | ### Other Known Limitations |
| |
|
| | [More Information Needed] |
| |
|
| | ## Additional Information |
| |
|
| | ### Dataset Curators |
| |
|
| | [More Information Needed] |
| |
|
| | ### Licensing Information |
| |
|
| | [More Information Needed] |
| |
|
| | ### Citation Information |
| |
|
| | ``` |
| | @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} |
| | } |
| | ``` |
| |
|
| | ### Contributions |
| |
|
| | Thanks to [@jonatasgrosman](https://github.com/jonatasgrosman) for adding this dataset. |
