Dataset Card for allenai/wmt22_african
Table of Contents
- Dataset Description
- Dataset Structure
- Dataset Creation
- Considerations for Using the Data
- Additional Information
Dataset Description
- Homepage: https://www.statmt.org/wmt22/large-scale-multilingual-translation-task.html
- Repository: [Needs More Information]
- Paper: [Needs More Information]
- Leaderboard: [Needs More Information]
- Point of Contact: [Needs More Information]
Dataset Summary
This dataset was created based on metadata for mined bitext released by Meta AI. It contains bitext for 248 pairs for the African languages that are part of the 2022 WMT Shared Task on Large Scale Machine Translation Evaluation for African Languages.
How to use the data
There are two ways to access the data:
- Via the Hugging Face Python datasets library
from datasets import load_dataset
dataset = load_dataset("allenai/wmt22_african")
- Clone the git repo
git lfs install
git clone https://huggingface.co/datasets/allenai/wmt22_african
Supported Tasks and Leaderboards
This dataset is one of resources allowed under the Constrained Track for the 2022 WMT Shared Task on Large Scale Machine Translation Evaluation for African Languages.
Languages
Focus languages
| Language | Code |
|---|---|
| Afrikaans | afr |
| Amharic | amh |
| Chichewa | nya |
| Nigerian Fulfulde | fuv |
| Hausa | hau |
| Igbo | ibo |
| Kamba | kam |
| Kinyarwanda | kin |
| Lingala | lin |
| Luganda | lug |
| Luo | luo |
| Northern Sotho | nso |
| Oroma | orm |
| Shona | sna |
| Somali | som |
| Swahili | swh |
| Swati | ssw |
| Tswana | tsn |
| Umbundu | umb |
| Wolof | wol |
| Xhosa | xho |
| Xitsonga | tso |
| Yoruba | yor |
| Zulu | zul |
Colonial linguae francae: English - eng, French - fra
Dataset Structure
The dataset contains gzipped tab delimited text files for each direction. Each text file contains lines with parallel sentences.
Data Instances
The dataset contains 248 language pairs.
Sentence counts for each pair can be found here.
Data Fields
Every instance for a language pair contains the following fields: 'translation' (containing sentence pairs), 'laser_score', 'source_sentence_lid', 'target_sentence_lid', where 'lid' is language classification probability.
Example:
{
'translation':
{
'afr': 'In Mei 2007, in ooreenstemming met die spesifikasies van die Java Gemeenskapproses, het Sun Java tegnologie geherlisensieer onder die GNU General Public License.',
'eng': 'As of May 2007, in compliance with the specifications of the Java Community Process, Sun relicensed most of its Java technologies under the GNU General Public License.'
},
'laser_score': 1.0717015266418457,
'source_sentence_lid': 0.9996600151062012,
'target_sentence_lid': 0.9972000122070312
}
Data Splits
The data is not split into train, dev, and test.
Dataset Creation
Curation Rationale
Parallel sentences from monolingual data in Common Crawl and ParaCrawl were identified via Language-Agnostic Sentence Representation (LASER) encoders.
Source Data
Initial Data Collection and Normalization
Monolingual data was obtained from Common Crawl and ParaCrawl.
Who are the source language producers?
Contributors to web text in Common Crawl and ParaCrawl.
Annotations
Annotation process
The data was not human annotated. The metadata used to create the dataset can be found here: https://github.com/facebookresearch/LASER/tree/main/data/wmt22_african
Who are the annotators?
The data was not human annotated. Parallel text from Common Crawl and Para Crawl monolingual data were identified automatically via LASER encoders.
Personal and Sensitive Information
[Needs More Information]
Considerations for Using the Data
Social Impact of Dataset
This dataset provides data for training machine learning systems for many languages that have low resources available for NLP.
Discussion of Biases
Biases in the data have not been studied.
Other Known Limitations
[Needs More Information]
Additional Information
Dataset Curators
[Needs More Information]
Licensing Information
The dataset is released under the terms of ODC-BY. By using this, you are also bound by the Internet Archive Terms of Use in respect of the content contained in the dataset.
Citation Information
NLLB Team et al, No Language Left Behind: Scaling Human-Centered Machine Translation, Arxiv, 2022.
Contributions
We thank the AllenNLP team at AI2 for hosting and releasing this data, including Akshita Bhagia (for engineering efforts to create the huggingface dataset), and Jesse Dodge (for organizing the connection).