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.",
}