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
config_names:
  - af
  - all_languages
  - 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
tags:
  - paraphrase-generation
dataset_info:
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  - config_name: la
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configs:
  - config_name: af
    data_files:
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        path: af/train-*
  - config_name: all_languages
    data_files:
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        path: all_languages/train-*
    default: true
  - config_name: ar
    data_files:
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        path: ar/train-*
  - config_name: az
    data_files:
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        path: az/train-*
  - config_name: be
    data_files:
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        path: be/train-*
  - config_name: ber
    data_files:
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        path: ber/train-*
  - config_name: bg
    data_files:
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        path: bg/train-*
  - config_name: bn
    data_files:
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        path: bn/train-*
  - config_name: br
    data_files:
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        path: br/train-*
  - config_name: ca
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        path: ca/train-*
  - config_name: cbk
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        path: cbk/train-*
  - config_name: cmn
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        path: cmn/train-*
  - config_name: cs
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        path: cs/train-*
  - config_name: da
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        path: da/train-*
  - config_name: de
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        path: de/train-*
  - config_name: el
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  - config_name: en
    data_files:
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        path: en/train-*
  - config_name: eo
    data_files:
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        path: eo/train-*
  - config_name: es
    data_files:
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        path: es/train-*
  - config_name: et
    data_files:
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        path: et/train-*
  - config_name: eu
    data_files:
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        path: eu/train-*
  - config_name: fi
    data_files:
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        path: fi/train-*
  - config_name: fr
    data_files:
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        path: fr/train-*
  - config_name: gl
    data_files:
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        path: gl/train-*
  - config_name: gos
    data_files:
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        path: gos/train-*
  - config_name: he
    data_files:
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        path: he/train-*
  - config_name: hi
    data_files:
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        path: hi/train-*
  - config_name: hr
    data_files:
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        path: hr/train-*
  - config_name: hu
    data_files:
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        path: hu/train-*
  - config_name: hy
    data_files:
      - split: train
        path: hy/train-*
  - config_name: ia
    data_files:
      - split: train
        path: ia/train-*

Dataset Card for TaPaCo Corpus

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 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 for adding this dataset.

Datasets:

community-datasets
/

tapaco

Dataset Card for TaPaCo Corpus

Table of Contents

Dataset Description

Dataset Summary

Supported Tasks and Leaderboards

Languages

Dataset Structure

Data Instances

Data Fields

Data Splits

Dataset Creation

Curation Rationale

Source Data

Initial Data Collection and Normalization

Who are the source language producers?

Annotations

Annotation process

Who are the annotators?

Personal and Sensitive Information

Considerations for Using the Data

Social Impact of Dataset

Discussion of Biases

Other Known Limitations

Additional Information

Dataset Curators

Licensing Information

Citation Information

Contributions