Datasets:
Tasks:
Translation
Size Categories:
1K<n<10K
Language Creators:
expert-generated
Annotations Creators:
expert-generated
Source Datasets:
original
License:
metadata
annotations_creators:
- expert-generated
language:
- en
- fr
- am
- bm
- bbj
- ee
- fon
- ha
- ig
- lg
- mos
- ny
- pcm
- rw
- sn
- sw
- tn
- tw
- wo
- xh
- yo
- zu
language_creators:
- expert-generated
license:
- cc-by-nc-4.0
multilinguality:
- translation
- multilingual
pretty_name: mafand
size_categories:
- 1K<n<10K
source_datasets:
- original
tags:
- news, mafand, masakhane
task_categories:
- translation
task_ids: []
Dataset Card for MAFAND
Table of Contents
- Dataset Description
- Dataset Structure
- Dataset Creation
- Considerations for Using the Data
- Additional Information
Dataset Description
- Homepage: https://github.com/masakhane-io/lafand-mt
- Repository: https://github.com/masakhane-io/lafand-mt
- Paper: https://aclanthology.org/2022.naacl-main.223/
- Leaderboard: [Needs More Information]
- Point of Contact: David Adelani
Dataset Summary
MAFAND-MT is the largest MT benchmark for African languages in the news domain, covering 21 languages.
Supported Tasks and Leaderboards
Machine Translation
Languages
The languages covered are:
- Amharic
- Bambara
- Ghomala
- Ewe
- Fon
- Hausa
- Igbo
- Kinyarwanda
- Luganda
- Luo
- Mossi
- Nigerian-Pidgin
- Chichewa
- Shona
- Swahili
- Setswana
- Twi
- Wolof
- Xhosa
- Yoruba
- Zulu
Dataset Structure
Data Instances
>>> from datasets import load_dataset
>>> data = load_dataset('masakhane/mafand', 'en-yor')
{"translation": {"src": "President Buhari will determine when to lift lockdown – Minister", "tgt": "Ààrẹ Buhari ló lè yóhùn padà lórí ètò kónílégbélé – Mínísítà"}}
{"translation": {"en": "President Buhari will determine when to lift lockdown – Minister", "yo": "Ààrẹ Buhari ló lè yóhùn padà lórí ètò kónílégbélé – Mínísítà"}}
Data Fields
- "translation": name of the task
- "src" : source language e.g en
- "tgt": target language e.g yo
Data Splits
Train/dev/test split
| language | Train | Dev | Test |
|---|---|---|---|
| amh | - | 899 | 1037 |
| bam | 3302 | 1484 | 1600 |
| bbj | 2232 | 1133 | 1430 |
| ewe | 2026 | 1414 | 1563 |
| fon | 2637 | 1227 | 1579 |
| hau | 5865 | 1300 | 1500 |
| ibo | 6998 | 1500 | 1500 |
| kin | - | 460 | 1006 |
| lug | 4075 | 1500 | 1500 |
| luo | 4262 | 1500 | 1500 |
| mos | 2287 | 1478 | 1574 |
| nya | - | 483 | 1004 |
| pcm | 4790 | 1484 | 1574 |
| sna | - | 556 | 1005 |
| swa | 30782 | 1791 | 1835 |
| tsn | 2100 | 1340 | 1835 |
| twi | 3337 | 1284 | 1500 |
| wol | 3360 | 1506 | 1500 |
| xho | - | 486 | 1002 |
| yor | 6644 | 1544 | 1558 |
| zul | 3500 | 1239 | 998 |
Dataset Creation
Curation Rationale
MAFAND was created from the news domain, translated from English or French to an African language
Source Data
Initial Data Collection and Normalization
[Needs More Information]
Who are the source language producers?
Annotations
Annotation process
[Needs More Information]
Who are the annotators?
Masakhane members
Personal and Sensitive Information
[Needs More Information]
Considerations for Using the Data
Social Impact of Dataset
[Needs More Information]
Discussion of Biases
[Needs More Information]
Other Known Limitations
[Needs More Information]
Additional Information
Dataset Curators
[Needs More Information]
Licensing Information
Citation Information
@inproceedings{adelani-etal-2022-thousand,
title = "A Few Thousand Translations Go a Long Way! Leveraging Pre-trained Models for {A}frican News Translation",
author = "Adelani, David and
Alabi, Jesujoba and
Fan, Angela and
Kreutzer, Julia and
Shen, Xiaoyu and
Reid, Machel and
Ruiter, Dana and
Klakow, Dietrich and
Nabende, Peter and
Chang, Ernie and
Gwadabe, Tajuddeen and
Sackey, Freshia and
Dossou, Bonaventure F. P. and
Emezue, Chris and
Leong, Colin and
Beukman, Michael and
Muhammad, Shamsuddeen and
Jarso, Guyo and
Yousuf, Oreen and
Niyongabo Rubungo, Andre and
Hacheme, Gilles and
Wairagala, Eric Peter and
Nasir, Muhammad Umair and
Ajibade, Benjamin and
Ajayi, Tunde and
Gitau, Yvonne and
Abbott, Jade and
Ahmed, Mohamed and
Ochieng, Millicent and
Aremu, Anuoluwapo and
Ogayo, Perez and
Mukiibi, Jonathan and
Ouoba Kabore, Fatoumata and
Kalipe, Godson and
Mbaye, Derguene and
Tapo, Allahsera Auguste and
Memdjokam Koagne, Victoire and
Munkoh-Buabeng, Edwin and
Wagner, Valencia and
Abdulmumin, Idris and
Awokoya, Ayodele and
Buzaaba, Happy and
Sibanda, Blessing and
Bukula, Andiswa and
Manthalu, Sam",
booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jul,
year = "2022",
address = "Seattle, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.naacl-main.223",
doi = "10.18653/v1/2022.naacl-main.223",
pages = "3053--3070",
abstract = "Recent advances in the pre-training for language models leverage large-scale datasets to create multilingual models. However, low-resource languages are mostly left out in these datasets. This is primarily because many widely spoken languages that are not well represented on the web and therefore excluded from the large-scale crawls for datasets. Furthermore, downstream users of these models are restricted to the selection of languages originally chosen for pre-training. This work investigates how to optimally leverage existing pre-trained models to create low-resource translation systems for 16 African languages. We focus on two questions: 1) How can pre-trained models be used for languages not included in the initial pretraining? and 2) How can the resulting translation models effectively transfer to new domains? To answer these questions, we create a novel African news corpus covering 16 languages, of which eight languages are not part of any existing evaluation dataset. We demonstrate that the most effective strategy for transferring both additional languages and additional domains is to leverage small quantities of high-quality translation data to fine-tune large pre-trained models.",
}