Datasets:
Sub-tasks:
semantic-similarity-classification
Multilinguality:
multilingual
Language Creators:
crowdsourced
Annotations Creators:
machine-generated
Source Datasets:
extended|other-tatoeba
License:
metadata
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
dataset_info:
- config_name: all_languages
features:
- name: paraphrase_set_id
dtype: string
- name: sentence_id
dtype: string
- name: paraphrase
dtype: string
- name: lists
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- name: tags
sequence: string
- name: language
dtype: string
splits:
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- config_name: af
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- config_name: ar
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- config_name: be
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- ug
- uk
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- wuu
- yue
Dataset Card for TaPaCo Corpus
Table of Contents
- Dataset Description
- Dataset Structure
- Dataset Creation
- Considerations for Using the Data
- Additional Information
Dataset Description
- Homepage: TaPaCo: A Corpus of Sentential Paraphrases for 73 Languages
- Paper: TaPaCo: A Corpus of Sentential Paraphrases for 73 Languages
- Data: https://doi.org/10.5281/zenodo.3707949
- Point of Contact: Yves Scherrer
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 othersentence_id: OPUS sentence idparaphrase: Sentential paraphrase in a given language for a given paraphrase_set_idlists: Contributors can add sentences to list in order to specify the original source of the datatags: Indicates morphological or phonological properties of the sentence when availablelanguage: 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 for adding this dataset.