Datasets:

masakhane
/

mafand

	---
	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-description)
	- [Dataset Summary](#dataset-summary)
	- [Supported Tasks](#supported-tasks-and-leaderboards)
	- [Languages](#languages)
	- [Dataset Structure](#dataset-structure)
	- [Data Instances](#data-instances)
	- [Data Fields](#data-instances)
	- [Data Splits](#data-instances)
	- [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: 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](https://dadelani.github.io/)

	### 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?

	- [Masakhane](https://github.com/masakhane-io/lafand-mt)
	- [Igbo](https://github.com/IgnatiusEzeani/IGBONLP/tree/master/ig_en_mt)
	- [Swahili](https://opus.nlpl.eu/GlobalVoices.php)
	- [Hausa](https://www.statmt.org/wmt21/translation-task.html)
	- [Yoruba](https://github.com/uds-lsv/menyo-20k_MT)

	### 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

	[CC-BY-4.0-NC](https://creativecommons.org/licenses/by-nc/4.0/)

	### 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.",
	}
	```