Datasets:
tapaco

	---
	annotations_creators:
	- machine-generated
	language_creators:
	- crowdsourced
	language:
	- af
	- ar
	- az
	- be
	- ber
	- bg
	- bn
	- br
	- ca
	- cbk
	- cmn
	- cs
	- da
	- de
	- el
	- en
	- eo
	- es
	- et
	- eu
	- fi
	- fr
	- gl
	- gos
	- he
	- hi
	- hr
	- hu
	- hy
	- ia
	- id
	- ie
	- io
	- is
	- it
	- ja
	- jbo
	- kab
	- ko
	- kw
	- la
	- lfn
	- lt
	- mk
	- mr
	- nb
	- nds
	- nl
	- orv
	- ota
	- pes
	- pl
	- pt
	- rn
	- ro
	- ru
	- sl
	- sr
	- sv
	- tk
	- tl
	- tlh
	- tok
	- tr
	- tt
	- ug
	- uk
	- ur
	- vi
	- vo
	- war
	- wuu
	- yue
	license:
	- cc-by-2.0
	multilinguality:
	- multilingual
	size_categories:
	- 100K<n<1M
	- 10K<n<100K
	- 1K<n<10K
	- 1M<n<10M
	- n<1K
	source_datasets:
	- extended\|other-tatoeba
	task_categories:
	- text2text-generation
	- translation
	- text-classification
	task_ids:
	- semantic-similarity-classification
	paperswithcode_id: tapaco
	pretty_name: TaPaCo Corpus
	tags:
	- paraphrase-generation
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	- uk
	- ur
	- vi
	- vo
	- war
	- wuu
	- yue
	---

	# Dataset Card for TaPaCo Corpus

	## 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: [TaPaCo: A Corpus of Sentential Paraphrases for 73 Languages](https://zenodo.org/record/3707949#.X9Dh0cYza3I)
	- Paper: [TaPaCo: A Corpus of Sentential Paraphrases for 73 Languages](https://www.aclweb.org/anthology/2020.lrec-1.848.pdf)
	- Data: https://doi.org/10.5281/zenodo.3707949
	- Point of Contact: [Yves Scherrer](https://blogs.helsinki.fi/yvesscherrer/)

	### Dataset Summary
	A freely available paraphrase corpus for 73 languages extracted from the Tatoeba database.
	Tatoeba is a crowdsourcing project mainly geared towards language learners. Its aim is to provide example sentences
	and translations for particular linguistic constructions and words. The paraphrase corpus is created by populating a
	graph with Tatoeba sentences and equivalence links between sentences “meaning the same thing”. This graph is then
	traversed to extract sets of paraphrases. Several language-independent filters and pruning steps are applied to
	remove uninteresting sentences. A manual evaluation performed on three languages shows that between half and three
	quarters of inferred paraphrases are correct and that most remaining ones are either correct but trivial,
	or near-paraphrases that neutralize a morphological distinction. The corpus contains a total of 1.9 million
	sentences, with 200 – 250 000 sentences per language. It covers a range of languages for which, to our knowledge,
	no other paraphrase dataset exists.

	### Supported Tasks and Leaderboards
	Paraphrase detection and generation have become popular tasks in NLP
	and are increasingly integrated into a wide variety of common downstream tasks such as machine translation
	, information retrieval, question answering, and semantic parsing. Most of the existing datasets
	cover only a single language – in most cases English – or a small number of languages. Furthermore, some paraphrase
	datasets focus on lexical and phrasal rather than sentential paraphrases, while others are created (semi
	-)automatically using machine translation.

	The number of sentences per language ranges from 200 to 250 000, which makes the dataset
	more suitable for fine-tuning and evaluation purposes than
	for training. It is well-suited for multi-reference evaluation
	of paraphrase generation models, as there is generally not a
	single correct way of paraphrasing a given input sentence.

	### Languages

	The dataset contains paraphrases in Afrikaans, Arabic, Azerbaijani, Belarusian, Berber languages, Bulgarian, Bengali
	, Breton, Catalan; Valencian, Chavacano, Mandarin, Czech, Danish, German, Greek, Modern (1453-), English, Esperanto
	, Spanish; Castilian, Estonian, Basque, Finnish, French, Galician, Gronings, Hebrew, Hindi, Croatian, Hungarian
	, Armenian, Interlingua (International Auxiliary Language Association), Indonesian, Interlingue; Occidental, Ido
	, Icelandic, Italian, Japanese, Lojban, Kabyle, Korean, Cornish, Latin, Lingua Franca Nova\t, Lithuanian, Macedonian
	, Marathi, Bokmål, Norwegian; Norwegian Bokmål, Low German; Low Saxon; German, Low; Saxon, Low, Dutch; Flemish, ]Old
	Russian, Turkish, Ottoman (1500-1928), Iranian Persian, Polish, Portuguese, Rundi, Romanian; Moldavian; Moldovan,
	Russian, Slovenian, Serbian, Swedish, Turkmen, Tagalog, Klingon; tlhIngan-Hol, Toki Pona, Turkish, Tatar,
	Uighur; Uyghur, Ukrainian, Urdu, Vietnamese, Volapük, Waray, Wu Chinese and Yue Chinese

	## Dataset Structure

	### Data Instances
	Each data instance corresponds to a paraphrase, e.g.:
	```
	{
	'paraphrase_set_id': '1483',
	'sentence_id': '5778896',
	'paraphrase': 'Ɣremt adlis-a.',
	'lists': ['7546'],
	'tags': [''],
	'language': 'ber'
	}
	```

	### Data Fields
	Each dialogue instance has the following fields:
	- `paraphrase_set_id`: a running number that groups together all sentences that are considered paraphrases of each
	other
	- `sentence_id`: OPUS sentence id
	- `paraphrase`: Sentential paraphrase in a given language for a given paraphrase_set_id
	- `lists`: Contributors can add sentences to list in order to specify the original source of the data
	- `tags`: Indicates morphological or phonological properties of the sentence when available
	- `language`: Language identifier, one of the 73 languages that belong to this dataset.

	### Data Splits

	The dataset is having a single `train` split, contains a total of 1.9 million sentences, with 200 – 250 000
	sentences per language

	## 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

	Creative Commons Attribution 2.0 Generic

	### Citation Information

	```
	@dataset{scherrer_yves_2020_3707949,
	author = {Scherrer, Yves},
	title = {{TaPaCo: A Corpus of Sentential Paraphrases for 73 Languages}},
	month = mar,
	year = 2020,
	publisher = {Zenodo},
	version = {1.0},
	doi = {10.5281/zenodo.3707949},
	url = {https://doi.org/10.5281/zenodo.3707949}
	}
	```

	### Contributions

	Thanks to [@pacman100](https://github.com/pacman100) for adding this dataset.