Datasets:
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 [Needs More Information]
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 (didelani@lsv.uni-saarland.de)
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
{"translation": {"src": "--- President Buhari will determine when to lift lockdown Minister", "tgt": "--- ��r� Buhari l� l� y�h�n pad� l�r� �t� k�n�l�gb�l� M�n�s�t�"}}
{"translation": {"en": "--- President Buhari will determine when to lift lockdown Minister", "yo": "--- ��r� Buhari l� l� y�h�n pad� l�r� �t� k�n�l�gb�l� M�n�s�t�"}}
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?
Masakhane Igbo Swahili Hausa Yoruba
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.", }