@inproceedings{sang-etal-2025-federated,
title = "Federated Meta-Learning for Low-Resource Translation of {K}irundi",
author = "Sang, Kyle Rui and
Rabbani, Tahseen and
Zhou, Tianyi",
editor = "Holdt, {\v{S}}pela Arhar and
Ilinykh, Nikolai and
Scalvini, Barbara and
Bruton, Micaella and
Debess, Iben Nyholm and
Tudor, Crina Madalina",
booktitle = "Proceedings of the Third Workshop on Resources and Representations for Under-Resourced Languages and Domains (RESOURCEFUL-2025)",
month = mar,
year = "2025",
address = "Tallinn, Estonia",
publisher = "University of Tartu Library, Estonia",
url = "https://aclanthology.org/2025.resourceful-1.34/",
pages = "190--194",
ISBN = "978-9908-53-121-2",
abstract = "In this work, we reframe multilingual neural machine translation (NMT) as a federated meta-learning problem and introduce a translation dataset for the low-resource Kirundi language. We aggregate machine translation models () locally trained on varying (but related) source languages to produce a global meta-model that encodes abstract representations of key semantic structures relevant to the parent languages. We then use the Reptile algorithm and Optuna fine-tuning to fit the global model onto a target language. The target language may live outside the subset of parent languages (such as closely-related dialects or sibling languages), which is particularly useful for languages with limitedly available sentence pairs. We first develop a novel dataset of Kirundi-English sentence pairs curated from Biblical translation. We then demonstrate that a federated learning approach can produce a tiny 4.8M Kirundi translation model and a stronger NLLB-600M model which performs well on both our Biblical corpus and the FLORES-200 Kirundi corpus."
}<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="sang-etal-2025-federated">
<titleInfo>
<title>Federated Meta-Learning for Low-Resource Translation of Kirundi</title>
</titleInfo>
<name type="personal">
<namePart type="given">Kyle</namePart>
<namePart type="given">Rui</namePart>
<namePart type="family">Sang</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Tahseen</namePart>
<namePart type="family">Rabbani</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Tianyi</namePart>
<namePart type="family">Zhou</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2025-03</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the Third Workshop on Resources and Representations for Under-Resourced Languages and Domains (RESOURCEFUL-2025)</title>
</titleInfo>
<name type="personal">
<namePart type="given">Špela</namePart>
<namePart type="given">Arhar</namePart>
<namePart type="family">Holdt</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Nikolai</namePart>
<namePart type="family">Ilinykh</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Barbara</namePart>
<namePart type="family">Scalvini</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Micaella</namePart>
<namePart type="family">Bruton</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Iben</namePart>
<namePart type="given">Nyholm</namePart>
<namePart type="family">Debess</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Crina</namePart>
<namePart type="given">Madalina</namePart>
<namePart type="family">Tudor</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>University of Tartu Library, Estonia</publisher>
<place>
<placeTerm type="text">Tallinn, Estonia</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
<identifier type="isbn">978-9908-53-121-2</identifier>
</relatedItem>
<abstract>In this work, we reframe multilingual neural machine translation (NMT) as a federated meta-learning problem and introduce a translation dataset for the low-resource Kirundi language. We aggregate machine translation models () locally trained on varying (but related) source languages to produce a global meta-model that encodes abstract representations of key semantic structures relevant to the parent languages. We then use the Reptile algorithm and Optuna fine-tuning to fit the global model onto a target language. The target language may live outside the subset of parent languages (such as closely-related dialects or sibling languages), which is particularly useful for languages with limitedly available sentence pairs. We first develop a novel dataset of Kirundi-English sentence pairs curated from Biblical translation. We then demonstrate that a federated learning approach can produce a tiny 4.8M Kirundi translation model and a stronger NLLB-600M model which performs well on both our Biblical corpus and the FLORES-200 Kirundi corpus.</abstract>
<identifier type="citekey">sang-etal-2025-federated</identifier>
<location>
<url>https://aclanthology.org/2025.resourceful-1.34/</url>
</location>
<part>
<date>2025-03</date>
<extent unit="page">
<start>190</start>
<end>194</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Federated Meta-Learning for Low-Resource Translation of Kirundi
%A Sang, Kyle Rui
%A Rabbani, Tahseen
%A Zhou, Tianyi
%Y Holdt, Špela Arhar
%Y Ilinykh, Nikolai
%Y Scalvini, Barbara
%Y Bruton, Micaella
%Y Debess, Iben Nyholm
%Y Tudor, Crina Madalina
%S Proceedings of the Third Workshop on Resources and Representations for Under-Resourced Languages and Domains (RESOURCEFUL-2025)
%D 2025
%8 March
%I University of Tartu Library, Estonia
%C Tallinn, Estonia
%@ 978-9908-53-121-2
%F sang-etal-2025-federated
%X In this work, we reframe multilingual neural machine translation (NMT) as a federated meta-learning problem and introduce a translation dataset for the low-resource Kirundi language. We aggregate machine translation models () locally trained on varying (but related) source languages to produce a global meta-model that encodes abstract representations of key semantic structures relevant to the parent languages. We then use the Reptile algorithm and Optuna fine-tuning to fit the global model onto a target language. The target language may live outside the subset of parent languages (such as closely-related dialects or sibling languages), which is particularly useful for languages with limitedly available sentence pairs. We first develop a novel dataset of Kirundi-English sentence pairs curated from Biblical translation. We then demonstrate that a federated learning approach can produce a tiny 4.8M Kirundi translation model and a stronger NLLB-600M model which performs well on both our Biblical corpus and the FLORES-200 Kirundi corpus.
%U https://aclanthology.org/2025.resourceful-1.34/
%P 190-194
Markdown (Informal)
[Federated Meta-Learning for Low-Resource Translation of Kirundi](https://aclanthology.org/2025.resourceful-1.34/) (Sang et al., RESOURCEFUL 2025)
ACL
- Kyle Rui Sang, Tahseen Rabbani, and Tianyi Zhou. 2025. Federated Meta-Learning for Low-Resource Translation of Kirundi. In Proceedings of the Third Workshop on Resources and Representations for Under-Resourced Languages and Domains (RESOURCEFUL-2025), pages 190–194, Tallinn, Estonia. University of Tartu Library, Estonia.