@inproceedings{etori-etal-2026-afrimmt,
    title = "{A}fri{MMT}-{EA}: Multi-domain Machine Translation for Low-Resource {E}ast {A}frican Languages",
    author = "Etori, Naome A  and
      Ezema, Kelechi  and
      Robinson, Nathaniel Romney  and
      David, Davis  and
      Kondoro, Alfred Malengo  and
      Makori, Elisha Ondieki  and
      Mollel, Michael Samwel  and
      Gini, Maria",
    editor = "Demberg, Vera  and
      Inui, Kentaro  and
      Marquez, Llu{\'i}s",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EACL} 2026",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-eacl/2026.findings-eacl.179/",
    pages = "3459--3492",
    ISBN = "979-8-89176-386-9",
    abstract = "Despite remarkable progress in multilingual machine translation (MT), the majority of African{---}especially East African{---}languages remain significantly underrepresented both in benchmark datasets and state-of-the-art (SOTA) MT models. This persistent exclusion from mainstream technologies not only limits equitable access, but constrains the development of tools that accurately reflect the region{'}s linguistic and cultural diversity. Recent advances in open-source large language models have demonstrated strong multilingual MT capabilities through data-efficient adaptation strategies. However, little work has explored their potential for low-resource African languages. We introduce AfriMMT-EA, the first highly multilingual benchmark and MT dataset for East African languages. Our datasets comprise 54 local languages across five East African countries. We used these data to fine-tune two multilingual versions of Gemma-3. We compare models' performance on these languages with larger off-the-shelf baselines. We release our data and models, in the interest of advancing MT for these low-resource languages and their communities."
}