README.md

---
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}
}
```