language:
- eng
- afr
- nbl
- xho
- zul
- sot
- nso
- tsn
- ssw
- ven
- tso
license: cc-by-4.0
task_categories:
- sentence-similarity
- translation
pretty_name: The Vuk'uzenzele South African Multilingual Corpus
tags:
- multilingual
- government
arxiv: 2303.0375
configs:
- config_name: afr-nbl
data_files:
- split: train
path: afr-nbl/train-*
- split: test
path: afr-nbl/test-*
- config_name: afr-tsn
data_files:
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path: afr-tsn/train-*
- split: test
path: afr-tsn/test-*
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data_files:
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path: afr-tso/train-*
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path: afr-tso/test-*
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path: afr-ven/train-*
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path: afr-ven/test-*
- config_name: afr-xho
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path: afr-xho/train-*
- split: test
path: afr-xho/test-*
- config_name: default
data_files:
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path: data/train-*
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path: data/test-*
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data_files:
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path: eng-nso/train-*
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path: eng-nso/test-*
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path: eng-sot/train-*
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path: eng-ssw/train-*
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path: eng-ssw/test-*
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path: eng-zul/train-*
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path: eng-zul/test-*
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path: nbl-nso/test-*
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path: nbl-sot/train-*
- split: test
path: nbl-sot/test-*
- config_name: nbl-ssw
data_files:
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- split: test
path: nbl-ssw/test-*
- config_name: nbl-zul
data_files:
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- split: test
path: nbl-zul/test-*
- config_name: nso-tsn
data_files:
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path: nso-tsn/test-*
- config_name: nso-tso
data_files:
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- split: test
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- config_name: nso-ven
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- config_name: ven-zul
data_files:
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- split: test
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The Vuk'uzenzele South African Multilingual Corpus
Github: https://github.com/dsfsi/vukuzenzele-nlp/
Give Feedback 📑: DSFSI Resource Feedback Form
About
The dataset was obtained from the South African government magazine Vuk'uzenzele, created by the Government Communication and Information System (GCIS). The original raw PDFS were obtatined from the Vuk'uzenzele website.
The datasets contain government magazine editions in 11 languages, namely:
Language | Code | Language | Code |
---|---|---|---|
English | (eng) | Sepedi | (sep) |
Afrikaans | (afr) | Setswana | (tsn) |
isiNdebele | (nbl) | Siswati | (ssw) |
isiXhosa | (xho) | Tshivenda | (ven) |
isiZulu | (zul) | Xitstonga | (tso) |
Sesotho | (nso) |
Available pairings
The alignment direction is bidrectional, i.e. xho-zul is zul-xho
afr-eng; afr-nbl; afr-nso; afr-sot; afr-ssw; afr-tsn; afr-tso; afr-ven; afr-xho; afr-zul
eng-nbl; eng-nso; eng-sot ;eng-ssw; eng-tsn; eng-tso; eng-ven; eng-xho; eng-zul
nbl-nso; nbl-sot; nbl-ssw; nbl-tsn; nbl-tso; nbl-ven; nbl-xho; nbl-zul
nso-sot; nso-ssw; nso-tsn; nso-tso; nso-ven; nso-xho; nso-zul
sot-ssw; sot-tsn; sot-tso; sot-ven; sot-xho; sot-zul
ssw-tsn; ssw-tso; ssw-ven; ssw-xho; ssw-zul
tsn-tso; tsn-ven; tsn-xho; tsn-zul
tso-ven; tso-xho; tso-zul
ven-xho; ven-zul
xho-zul
Disclaimer
This dataset contains machine-readable data extracted from PDF documents, from https://www.vukuzenzele.gov.za/, provided by the Government Communication Information System (GCIS). While efforts were made to ensure the accuracy and completeness of this data, there may be errors or discrepancies between the original publications and this dataset. No warranties, guarantees or representations are given in relation to the information contained in the dataset. The members of the Data Science for Societal Impact Research Group bear no responsibility and/or liability for any such errors or discrepancies in this dataset. The Government Communication Information System (GCIS) bears no responsibility and/or liability for any such errors or discrepancies in this dataset. It is recommended that users verify all information contained herein before making decisions based upon this information.
Datasets
The datasets consist of pairwise sentence aligned data. There are 55 distinct datasets of paired sentences. The data is obtained by comparing LASER embeddings of sentence tokens between 2 languages. If the similarity is high, the sentences are deemed semantic equivalents of one another and the observation is outputted.
Naming convention:
The naming structure of the pairwise_sentence_aligned folder is aligned-{src_lang_code}-{tgt_lang_code}.csv
.
For example, aligned-afr-zul.csv
is the aligned sentences between Afrikaans and isiZulu.
The data is in .csv format and the columns are src_text
,tgt_text
,cosine_score
where:
src_text
is the source sentencetgt_text
is the target sentencecosine_score
is the cosine similarity score obtained by comparing the sentence embeddings, it ranges from 0 to 1
Note: The notion of source (src) and target (tgt) are only necessary for distinction between the languages used in the aligned pair, as the sentence semantics should be bidirectional. (hallo <-> sawubona)
Citation
Vukosi Marivate, Andani Madodonga, Daniel Njini, Richard Lastrucci, Isheanesu Dzingirai, Jenalea Rajab. The Vuk'uzenzele South African Multilingual Corpus, 2023
@dataset{marivate_vukosi_2023_7598540, author = {Marivate, Vukosi and Njini, Daniel and Madodonga, Andani and Lastrucci, Richard and Dzingirai, Isheanesu Rajab, Jenalea}, title = {The Vuk'uzenzele South African Multilingual Corpus}, month = feb, year = 2023, publisher = {Zenodo}, doi = {10.5281/zenodo.7598539}, url = {https://doi.org/10.5281/zenodo.7598539} }
Licence
- Licence for Data - CC 4.0 BY