Datasets:
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 Structure
- Dataset Creation
- Considerations for Using the Data
- Additional Information
Dataset Description
- Homepage: ASSIN homepage
- Repository: ASSIN repository
- Paper: ASSIN: Evaluation of Semantic Similarity and Textual Inference
- Point of Contact: Erick Rocha Fonseca
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: aint64feature.premise: astringfeature.hypothesis: astringfeature.relatedness_score: afloat32feature.entailment_judgment: a classification label, with possible values includingNONE,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 for adding this dataset.