Dataset Preview Go to dataset viewer
paraphrase_set_id (string)sentence_id (string)paraphrase (string)lists (sequence)tags (sequence)language (string)
"0"
"5494428"
"Ek drink."
[ "" ]
[ "" ]
"af"
"0"
"5875986"
"Ek is dors."
[ "" ]
[ "" ]
"af"
"0"
"7986816"
"Ek drink water."
[ "" ]
[ "" ]
"af"
"40"
"5458722"
"Dis binnekort lente."
[ "" ]
[ "" ]
"af"
"40"
"5458724"
"Dit is lente."
[ "" ]
[ "" ]
"af"
"40"
"5458725"
"Die lente het teruggekom."
[ "" ]
[ "" ]
"af"
"40"
"5458726"
"Die lente kom nader."
[ "" ]
[ "" ]
"af"
"56"
"846136"
"Ek soek werk."
[ "" ]
[ "" ]
"af"
"56"
"1597519"
"Ek werk."
[ "" ]
[ "" ]
"af"
"56"
"5443512"
"Ek studeer."
[ "" ]
[ "" ]
"af"
"56"
"5997523"
"Ek is tans besig."
[ "" ]
[ "" ]
"af"
"88"
"675962"
"Dit is my vriend."
[ "" ]
[ "" ]
"af"
"88"
"753260"
"Dis my vriend."
[ "" ]
[ "" ]
"af"
"88"
"1357207"
"Dis my dogter."
[ "" ]
[ "" ]
"af"
"88"
"7986875"
"Sy is my meisie."
[ "" ]
[ "" ]
"af"
"92"
"1359766"
"My broer leef in Tokio."
[ "" ]
[ "" ]
"af"
"92"
"1598611"
"My broer woon in Tokio."
[ "" ]
[ "" ]
"af"
"94"
"1386997"
"Die Titanic het gesink op haar eerste vaart. Sy was 'n groot skip."
[ "" ]
[ "" ]
"af"
"94"
"1598593"
"Die Titanic het op sy eerste vaart gesink. Dit was 'n groot skip."
[ "" ]
[ "" ]
"af"
"98"
"1422322"
"Die landskap is awemrowend."
[ "" ]
[ "" ]
"af"
"98"
"1598552"
"Die landskap is asemrowend."
[ "" ]
[ "" ]
"af"
"106"
"1422336"
"Ons sal jou roep wanneer jou tafel gereed is."
[ "" ]
[ "" ]
"af"
"106"
"1422339"
"Ons sal u skakel wanneer u tafel gereed is."
[ "" ]
[ "" ]
"af"
"118"
"1424410"
"Hoe lank het jy daar gebly?"
[ "" ]
[ "" ]
"af"
"118"
"1424411"
"Hoe lank het u daar gebly?"
[ "" ]
[ "" ]
"af"
"155"
"1597561"
"Wag totdat die verkeerslig groen word."
[ "" ]
[ "" ]
"af"
"155"
"5757201"
"Wag totdat die lig groen is."
[ "" ]
[ "" ]
"af"
"167"
"983996"
"Hierdie hond is van my."
[ "" ]
[ "" ]
"af"
"167"
"1598592"
"Hierdie hond is myne."
[ "" ]
[ "" ]
"af"
"169"
"385198"
"Kan jy jou kar beweeg asseblief?"
[ "" ]
[ "" ]
"af"
"169"
"1598640"
"Kan jy asseblief jou kar skuif."
[ "" ]
[ "" ]
"af"
"169"
"2779458"
"Sal jy asseblief jou motor skuif?"
[ "" ]
[ "" ]
"af"
"170"
"384993"
"Hul het hul seun John benoem."
[ "" ]
[ "" ]
"af"
"170"
"1598642"
"Hulle het hulle seun John genoem."
[ "" ]
[ "" ]
"af"
"190"
"1611267"
"Van watter sportsoort hou jy die meeste?"
[ "" ]
[ "" ]
"af"
"190"
"2788467"
"Van watse sport hou jy die meeste van?"
[ "" ]
[ "" ]
"af"
"199"
"1706352"
"Ons het met die hysbak op en af gery."
[ "" ]
[ "" ]
"af"
"199"
"1857693"
"Ons het op en af met die hysbak gery."
[ "" ]
[ "" ]
"af"
"214"
"1735096"
"Jy moet my gewis more oggend bel."
[ "" ]
[ "" ]
"af"
"214"
"1735097"
"Maak seker dat jy my more oggend bel."
[ "" ]
[ "" ]
"af"
"295"
"1759574"
"Tom het gedink dit was onregverdig."
[ "" ]
[ "" ]
"af"
"295"
"1759576"
"Tom dog dat dit onregverdig was."
[ "" ]
[ "" ]
"af"
"310"
"1764509"
"Hulle het wyn."
[ "" ]
[ "" ]
"af"
"310"
"1764513"
"Sy het wyn."
[ "" ]
[ "" ]
"af"
"312"
"1764511"
"Jy het wyn."
[ "" ]
[ "" ]
"af"
"312"
"1764512"
"Julle het wyn."
[ "" ]
[ "" ]
"af"
"336"
"1766555"
"Sy is jonk."
[ "" ]
[ "" ]
"af"
"336"
"1766557"
"Hy is jonk."
[ "" ]
[ "" ]
"af"
"336"
"4874643"
"Sy was jonk."
[ "" ]
[ "" ]
"af"
"340"
"1766992"
"Hy kan 'n bietjie Engels praat."
[ "" ]
[ "" ]
"af"
"340"
"1766993"
"Hy kan nie veel Engels praat nie."
[ "" ]
[ "" ]
"af"
"353"
"1769347"
"Het u 'n selfoon?"
[ "" ]
[ "" ]
"af"
"353"
"1769348"
"Het julle 'n selfoon?"
[ "" ]
[ "" ]
"af"
"353"
"1769355"
"Het jy 'n selfoon?"
[ "" ]
[ "" ]
"af"
"379"
"1775789"
"Wat dink jy moet gedoen word daaroor?"
[ "" ]
[ "" ]
"af"
"379"
"1775790"
"Volgens jou wat moet daaroor gedoen word?"
[ "" ]
[ "" ]
"af"
"391"
"1783258"
"Water is belangrik."
[ "" ]
[ "" ]
"af"
"391"
"5945834"
"Die meer is groot."
[ "" ]
[ "" ]
"af"
"396"
"1784091"
"Waar is my pa?"
[ "" ]
[ "" ]
"af"
"396"
"5489351"
"Waar is pa?"
[ "" ]
[ "" ]
"af"
"397"
"1784122"
"Ons sal jou begrawe."
[ "" ]
[ "" ]
"af"
"397"
"1784124"
"Ons sal julle begrawe."
[ "" ]
[ "" ]
"af"
"423"
"1802672"
"Hou jou vriend van tee?"
[ "" ]
[ "" ]
"af"
"423"
"1802674"
"Hou jou vriendin van tee?"
[ "" ]
[ "" ]
"af"
"488"
"1823593"
"Watter soort boek wil jy he?"
[ "" ]
[ "" ]
"af"
"488"
"2788468"
"Watse boek wil jy hê?"
[ "" ]
[ "" ]
"af"
"525"
"1838401"
"Die oomblik wat ek haar gesien het, het ek haar herken."
[ "" ]
[ "" ]
"af"
"525"
"3437716"
"Ek het haar herken toe ek haar skaars gesien het."
[ "" ]
[ "" ]
"af"
"563"
"1854974"
"Ek werk by hierdie maatskappy."
[ "" ]
[ "" ]
"af"
"563"
"5491767"
"Ek werk vir hierdie onderneming."
[ "" ]
[ "" ]
"af"
"576"
"1857695"
"Het jy 'n vuurhoutjie?"
[ "" ]
[ "" ]
"af"
"576"
"1857696"
"Het u vuurhoutjies?"
[ "" ]
[ "" ]
"af"
"582"
"384916"
"Ek kan nie nou aan sy naam dink nie."
[ "" ]
[ "" ]
"af"
"582"
"1860077"
"Vir 'n oomblik het ek vergeet wat haar naam was."
[ "" ]
[ "" ]
"af"
"610"
"1915379"
"Sy is bly oor haar nuwe rok."
[ "" ]
[ "" ]
"af"
"610"
"6607409"
"Sy was bly met haar nuwe rok."
[ "" ]
[ "@needs native check" ]
"af"
"616"
"1918407"
"Hy het gepraat."
[ "" ]
[ "" ]
"af"
"616"
"1918408"
"Hy was besig om te praat."
[ "" ]
[ "" ]
"af"
"659"
"1974528"
"Waar bly jou oupa?"
[ "" ]
[ "" ]
"af"
"659"
"5897622"
"Waar woon jou oupa?"
[ "" ]
[ "" ]
"af"
"688"
"387068"
"Ek het laas nag hier aangekom."
[ "" ]
[ "" ]
"af"
"688"
"2019902"
"Ek het gister gekom."
[ "" ]
[ "" ]
"af"
"694"
"2045062"
"My vriend het my 'n brief geskryf waarin hy my gevra het of dit goed gaan met my."
[ "" ]
[ "" ]
"af"
"694"
"4263597"
"My vriend het vir my 'n brief geskryf om uit te vind hoe ek was."
[ "" ]
[ "" ]
"af"
"714"
"2060247"
"Skakel af die lig."
[ "" ]
[ "" ]
"af"
"714"
"2364248"
"Kan jy die ligte afskakel?"
[ "" ]
[ "" ]
"af"
"755"
"2345891"
"Is jou ma tuis?"
[ "" ]
[ "" ]
"af"
"755"
"5540661"
"Is jou ma hier?"
[ "" ]
[ "" ]
"af"
"946"
"2779400"
"Ek het vir Ann 'n pop gemaak."
[ "" ]
[ "" ]
"af"
"946"
"2779403"
"Ek het 'n pop vir Ann gemaak."
[ "" ]
[ "" ]
"af"
"948"
"2779407"
"Daar was 'n bus skedule op die muur."
[ "" ]
[ "" ]
"af"
"948"
"2779409"
"Op die muur was daar 'n bus skedule."
[ "" ]
[ "" ]
"af"
"1093"
"2783911"
"Ek sal dit terug gee."
[ "" ]
[ "" ]
"af"
"1093"
"2783919"
"Ek gee dit terug."
[ "" ]
[ "" ]
"af"
"1107"
"2783932"
"Ek is al gewoond daaraan teen nou."
[ "" ]
[ "" ]
"af"
"1107"
"2970746"
"Ek is nou al gewoond daaraan."
[ "" ]
[ "" ]
"af"
"1110"
"2783937"
"Ek sou nie so seker wees nie."
[ "" ]
[ "" ]
"af"
"1110"
"2784157"
"Ek sou nie te seker wees daaroor nie."
[ "" ]
[ "" ]
"af"
"1113"
"2783942"
"Ek sal die volgende bus vang."
[ "" ]
[ "" ]
"af"
"1113"
"2784140"
"Ek gaan op die volgende bus wees."
[ "" ]
[ "" ]
"af"
End of preview (truncated to 100 rows)

Dataset Card for TaPaCo Corpus

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.

Models trained or fine-tuned on tapaco