readme: ---
  license: afl-3.0
  ---
  
  ### 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.",
  }
  ```