Datasets:
assin

	---
	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.