Datasets:
Modalities:
Text
Formats:
parquet
Sub-tasks:
semantic-similarity-classification
Size:
1M - 10M
Tags:
paraphrase-generation
License:
annotations_creators: | |
- machine-generated | |
language_creators: | |
- crowdsourced | |
languages: | |
all_languages: | |
- 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 | |
- toki | |
- tr | |
- tt | |
- ug | |
- uk | |
- ur | |
- vi | |
- vo | |
- war | |
- wuu | |
- yue | |
af: | |
- af | |
ar: | |
- ar | |
az: | |
- az | |
be: | |
- be | |
ber: | |
- ber | |
bg: | |
- bg | |
bn: | |
- bn | |
br: | |
- br | |
ca: | |
- ca | |
cbk: | |
- cbk | |
cmn: | |
- cmn | |
cs: | |
- cs | |
da: | |
- da | |
de: | |
- de | |
el: | |
- el | |
en: | |
- en | |
eo: | |
- eo | |
es: | |
- es | |
et: | |
- et | |
eu: | |
- eu | |
fi: | |
- fi | |
fr: | |
- fr | |
gl: | |
- gl | |
gos: | |
- gos | |
he: | |
- he | |
hi: | |
- hi | |
hr: | |
- hr | |
hu: | |
- hu | |
hy: | |
- hy | |
ia: | |
- ia | |
id: | |
- id | |
ie: | |
- ie | |
io: | |
- io | |
is: | |
- is | |
it: | |
- it | |
ja: | |
- ja | |
jbo: | |
- jbo | |
kab: | |
- kab | |
ko: | |
- ko | |
kw: | |
- kw | |
la: | |
- la | |
lfn: | |
- lfn | |
lt: | |
- lt | |
mk: | |
- mk | |
mr: | |
- mr | |
nb: | |
- nb | |
nds: | |
- nds | |
nl: | |
- nl | |
orv: | |
- orv | |
ota: | |
- ota | |
pes: | |
- pes | |
pl: | |
- pl | |
pt: | |
- pt | |
rn: | |
- rn | |
ro: | |
- ro | |
ru: | |
- ru | |
sl: | |
- sl | |
sr: | |
- sr | |
sv: | |
- sv | |
tk: | |
- tk | |
tl: | |
- tl | |
tlh: | |
- tlh | |
toki: | |
- toki | |
tr: | |
- tr | |
tt: | |
- tt | |
ug: | |
- ug | |
uk: | |
- uk | |
ur: | |
- ur | |
vi: | |
- vi | |
vo: | |
- vo | |
war: | |
- war | |
wuu: | |
- wuu | |
yue: | |
- yue | |
licenses: | |
- cc-by-2-0 | |
multilinguality: | |
- multilingual | |
size_categories: | |
- n>1M | |
source_datasets: | |
- extended|other-tatoeba | |
task_categories: | |
- conditional-text-generation | |
- text-classification | |
task_ids: | |
- conditional-text-generation-other-given-a-sentence-generate-a-paraphrase-either-in-same-language-or-another-language | |
- machine-translation | |
- semantic-similarity-classification | |
# Dataset Card for TaPaCo Corpus | |
## Table of Contents | |
- [Dataset Description](#dataset-description) | |
- [Dataset Summary](#dataset-summary) | |
- [Supported Tasks](#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) | |
## 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) | |
- **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} | |
} | |
``` | |