|
--- |
|
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: 463513 |
|
num_examples: 2500 |
|
- name: test |
|
num_bytes: 374432 |
|
num_examples: 2000 |
|
- name: validation |
|
num_bytes: 91211 |
|
num_examples: 500 |
|
download_size: 749735 |
|
dataset_size: 929156 |
|
- 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: 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. |
