David Ifeoluwa Adelani

henok.ademtew@mila.quebec

Biographie

David Adelani est professeur adjoint en science informatique et lutte contre les inégalités à l’Université McGill, et membre académique principal à Mila – Institut québécois d'intelligence artificielle. Ses recherches se concentrent sur le traitement multilingue du langage naturel, avec un accent particulier sur les langues sous-dotées en ressources.

Étudiants actuels

Henok Ademtew

Maîtrise recherche - McGill

Stagiaire de recherche - McGill

romuald.ahomagnon@mila.quebec

victor.akinode@mila.quebec

Victor Akinode

Maîtrise recherche - McGill

virginia.ceccatelli@mila.quebec

Virginia Ceccatelli

Collaborateur·rice de recherche - McGill

luel.hagos-beyene@mila.quebec

David Guzmán Ramírez

Doctorat - McGill

david.guzman@mila.quebec

Stagiaire de recherche - McGill

nicolas.helmeyer@mila.quebec

Weibang Han

Stagiaire de recherche - McGill

weibang.han@mila.quebec

Nicolas Helmeyer

Stagiaire de recherche - McGill

Postdoctorat - McGill

yejin.jeon@mila.quebec

Senyu Li Li

Doctorat - McGill

senyu.li@mila.quebec

Mona Liu

Stagiaire de recherche - McGill

mona.liu@mila.quebec

alexander.luo@mail.ncgill.ca

Yinhan Lu

Maîtrise recherche - UdeM

Superviseur⋅e principal⋅e :

Bang Liu

yinhan.lu@mila.quebec

Stagiaire de recherche - McGill

Marie Maltais

Doctorat - McGill

marie.maltais@mila.quebec

damaris-stephanie.ndjebayi@mila.quebec

Damaris Stephanie NDJEBAYI

Doctorat - McGill

Tolúlọpẹ Ògúnrẹ̀mí

Stagiaire de recherche - Standford University

tolulope.ogunremi@mila.quebec

Juhyun Oh

Collaborateur·rice alumni - McGill

juhyun.oh@mila.quebec

alessio.tosolini@mila.quebec

Jessica Ojo

Collaborateur·rice alumni - McGill

jessica.ojo@mila.quebec

Stagiaire de recherche - McGill

pablo.tellez@mila.quebec

Alessio Tosolini

Stagiaire de recherche - McGill

Kosei Uemura

Stagiaire de recherche - McGill

kosei.uemura@mila.quebec

Vivek Verma

Maîtrise professionnelle - UdeM

vivek.verma@mila.quebec

Jessica Wang

Stagiaire de recherche - McGill

jessica.wang@mila.quebec

Tianyou Xie

Stagiaire de recherche - McGill

tianyou.xie@mila.quebec

Tianyi Xu Xu

Stagiaire de recherche - McGill

tiany12138@gmail.com

Peter Yu

Collaborateur·rice alumni - McGill

Publications

African Languages AI Safety Readiness Report

Israel Abebe Azime

Jesujoba O. Alabi

Tajuddeen Gwadabe

With the widespread adoption of large language models (LLMs) across languages and domains, AI safety has emerged as a critical global concer… (voir plus)n. However, due to resource constraints, research and development efforts tend to prioritize a limited number of high-resource languages, leaving many others, especially those spoken in Africa, underrepresented. While existing research on language "resource-ness" has largely examined whether sufficient resources are available to develop language technologies, the question of whether languages are adequately represented in AI safety research has received attention only more recently. This report examines the readiness of African languages for inclusion in AI safety discussions, drawing on existing literature and data. It considers four factors: the availability of AI-relevant resources, including text data; national AI policy support; performance on established text benchmarks; and speaker population. Based on these factors, languages are grouped into four readiness categories: fully ready, mostly ready, emerging, and foundational support needed. This classification is intended to inform the inclusion of African languages in AI safety benchmarks, highlight varying levels of preparedness, and guide strategic investment in data development, policy, and community engagement for underrepresented languages.

2026-10-18

arXiv (prépublication)

ELARSpeech: A 30K-Hour Dataset Across 675 Underrepresented Languages for Long-Tail Multilingual Speech Technologies

Sangmin Lee

Massively multilingual speech modeling has made remarkable progress in recent years, yet existing training resources and benchmarks still un… (voir plus)derrepresent much of the linguistic long tail. To address this gap, we introduce ELARSpeech, a large-scale multilingual speech resource comprising 30,202 hours of speech across 675 underrepresented languages, curated from the Endangered Languages Archive. Combining human-annotated and unlabeled recordings, the corpus spans diverse recording conditions, domains, and language families. Beyond corpus construction, our experiments show how ELARSpeech supports extending ASR and TTS capabilities to diverse underrepresented languages, with further benefits from pseudo-labeling unlabeled data. At the same time, our speech representation analysis reveals remaining limitations in transferring existing multilingual models to the linguistic long tail. Together, ELARSpeech provides both a resource and a testbed for extending multilingual speech technology further into the linguistic long tail. We publicly release the dataset, all trained ASR and TTS models, and the complete data construction pipeline to facilitate research on long-tail multilingual speech technologies.

2026-10-13

arXiv (prépublication)

The ÌròyìnSpeech Text Corpus: 24,905 Curated Yorùbá Sentences for Speech and Language Technology

Kọ́lá Túbọ̀sún

Anuoluwapo Aremu

Tolúlọpẹ́ Ògúnrẹ̀mí

Iroro Orife

ÌròyìnSpeech is a 42-hour, 80-speaker Yorùbá read-speech corpus whose audio has been distributed by ELRA since 2024. This paper describ… (voir plus)es the release of its text component: 24,905 unique, hand-verified, tone-marked Yorùbá sentences (275,897 tokens; 15,687 types), curated in 2022 as recording prompts. Roughly 11,000 sentences were adapted from openly licensed news material; the remainder were written in-house to broaden coverage beyond the religious translation that dominates existing Yorùbá corpora. Every sentence was checked by hand for tone-mark accuracy, edited for read-aloud clarity and a neutral register, and localised so that non-Yorùbá personal and place names appear in Yorùbá form. Preparing the text for release surfaced systematic Unicode normalisation failures affecting more than 60% of lines (with precomposed and decomposed forms of the same letter co-occurring within single sentences) which we document and correct. The corpus supports diacritic restoration, grapheme-to-phoneme conversion, TTS front-end development and orthographic research, and serves as a validated prompt set for new recording.

2026-10-03

arXiv (prépublication)

VoxSumm: A Multilingual Corpus of Long-Form Spoken News for Joint Summarization and Translation

Marie Maltais

Virginia Ceccatelli

Min Ma

As information increasingly traverses linguistic boundaries, users require concise cross-lingual representations of long-form content. Never… (voir plus)theless, long-document summarization research remains text-centric, whereas multilingual speech research has largely prioritized translation, preserving source content rather than compressing it. We address this methodological gap by formalizing joint speech summarization and translation (JSumT): the generation of a succinct, faithful target-language summary directly from a long spoken document in a source language. We additionally introduce VoxSumm, the first multilingual and cross-lingual benchmark for this task, comprising 10,045 BBC article-summary pairs across 24 languages and encompassing approximately 703 hours of speech data. Our evaluation of representative speech-language models reveals pronounced variation across models and generation settings: Gemini3.1-Pro demonstrates the greatest consistency, summarization into English generally surpasses generation into non-English target languages, and translating an entire document before summarization compounds instruction-following failures. Through the release of VoxSumm, we establish a foundation for developing and evaluating multilingual systems capable of jointly interpreting, compressing, and translating long-form speech.

2026-08-10

arXiv (prépublication)

AfriSUD: A Dependency Treebank Collection for Evaluating Models on African Languages

Happy Buzaaba

Cheikh Mouhamadou Bamba Dione

Sylvain Kahane

Kim Gerdes

Bruno Guillaume

Kevin Guan

Aremu Anuoluwapo

Naome A. Etori

Shamsuddeen Hassan Muhammad

Utitofon Inyang

Peter Nabende

David Sabiiti Bamutura

Andiswa Bukula

Chinedu Uchechukwu

Rooweither Mabuya

Idris Akinade

Christiane Fellbaum

Despite their linguistic diversity and global significance, African languages remain underrepresented in research and resources to support N… (voir plus)LP. We aim to bridge this gap by introducing AfriSUD, the first large-scale collection of syntactically annotated treebanks for nine diverse African languages spanning major language families and regions across Sub-Saharan Africa. Using the Surface-Syntactic Universal Dependencies (SUD) framework, our community-led effort provides high-quality, native-speaker verified data that capture typological key features such as agglutination and tone. We evaluate a range of models on AfriSUD for part-of-speech tagging and dependency parsing including non-transformer baselines, multilingual pretrained encoders, and LLMs. Our results reveal a significant syntax gap, where models still show clear limitations across the nine languages, suggesting that existing architectures may not fully capture the structural diversity of African-language syntax.

2026-06-09

arXiv (prépublication)

OpenBibleTTS: Large-Scale Speech Resources and TTS Models for Low-Resource Languages

David Guzmán

Luel Hagos Beyene

Jesujoba Oluwadara Alabi

Dietrich Klakow

Recent advances in neural text-to-speech (TTS) and multilingual speech generation have substantially improved synthetic speech quality, yet … (voir plus)these gains remain unevenly distributed across the world's languages. Existing models are still dominated by a small set of high-resource languages, while many studies of low-resource TTS are simulated on artificially downsampled high-resource corpora that do not reflect the orthographic variation and limited phonetic coverage encountered in genuinely underrepresented settings. As such, we introduce OpenBibleTTS, which is a large-scale benchmark for low-resource speech synthesis spanning 37 underrepresented languages. Moreover, a systematic comparison of various TTS architectures and large-scale speech generation models is conducted across in-domain Biblical text and out-of-domain material. Results show that no single system dominates across languages and metrics: Gemini-TTS achieves the highest listener ratings on most evaluated languages, but monolingual EveryVoice models trained on OpenBibleTTS remain strongest for intelligibility and are preferred in several African languages, while open from-scratch systems degrade sharply on out-of-domain text, revealing a persistent gap between broad multilingual coverage and reliable synthesis quality in underserved linguistic communities. We complement automatic evaluation with subjective human judgments, and open-source all processed datasets, alignments, and trained models to support future low-resource TTS research.

2026-06-07

arXiv (prépublication)

SpeechJBB: Probing Safety Alignment and Comprehension in Large Audio Language Models under Code-Switched Speech

Virginia Ceccatelli

Large audio language models (LALMs) are increasingly deployed in real-world applications, yet their safety alignment is still primarily eval… (voir plus)uated on monolingual, text-based harmful prompts. This leaves their generalizability under multilingual and spoken settings, particularly code-switched speech, largely underexplored. To address this gap, we introduce SpeechJBB, an audio jailbreak dataset for benchmarking across multiple state-of-the-art LALMs. The extent of safety weaknesses is further probed by introducing an augmented setting where phonologically plausible pseudo-words are inserted around safety-critical terms to simulate localized obfuscation. Across models, code-switched harmful audio yields substantially high jailbreak success rates (JSR), with non-English monolingual and non-English code-switched pairs exhibiting the highest attack success. Pseudo-word insertion further reduces refusal rates, which demonstrates that natural-sounding obfuscation can effectively bypass safety policies.

2026-06-03

arXiv (prépublication)

TukaBench: A Culturally Grounded Jailbreak Benchmark for African Languages

Victor Akinode

Senyu Li

Wassim Hamidouche

Waqas Zamir

Inbal Becker-Reshef

Safety evaluation of Large Language Models (LLMs) remains heavily English-centric, leaving Low-Resource Languages (LRLs), particularly Afric… (voir plus)an ones, critically underexplored. We introduce TUKABENCH, a jailbreak benchmark for seven African languages that extends JailbreakBench (JBB) beyond direct translation through four settings: human translation of JBB prompts, English adaptation to African contexts followed by human translation, human-curated prompts validated through interactions with GPT-5.2, and code-switched prompts combining English and African languages, isolating the effect of language, cultural grounding, and prompt evasiveness on model safety. Across closed and open models, prompting in African languages reduces refusal relative to English, with culturally adapted prompts leading to least refusal. The evaluation also surfaces two structural limitations: model comprehension failures and reduced LLM-as-a-judge reliability in LRLs. To capture the first, we introduce Deflection alongside Refused and Jailbroken; to assess the second, we validate outputs with human annotations, showing that judge-human agreement drops in lower-resource languages and less commonly supported scripts.

2026-05-30

arXiv (prépublication)

NaijaS2ST: A Multi-Accent Benchmark for Speech-to-Speech Translation in Low-Resource Nigerian Languages

Marie Maltais

Min Ma

Shamsuddeen Hassan Muhammad

Idris Abdulmumin

Maryam Ibrahim Mukhtar

Daud Abolade

Joel Okepefi

Johnson Sewedo

Speech translation for low-resource languages remains fundamentally limited by the scarcity of high-quality, diverse parallel speech data, a… (voir plus) challenge that is especially pronounced in African linguistic contexts. To address this, we introduce NaijaS2ST, a parallel speech translation dataset spanning Igbo, Hausa, Yor\`ub\'a, and Nigerian Pidgin paired with English. The dataset comprises approximately 50 hours of speech per language and captures substantial variation in speakers and accents, reflecting realistic multilingual and multi-accent conditions. With NaijaS2ST, we conduct a comprehensive benchmark of cascaded, end-to-end (E2E), and AudioLLM-based approaches across bidirectional translation settings. Our results show that audio LLMs with few-shot examples are more effective for speech-to-text translation than cascaded and end-to-end methods trained on fine-tuned data. However, for speech-to-speech translation, the cascaded and audio LLM paradigms yield comparable performance, indicating that there is still considerable room for improvement in developing targeted, task-specific models for this setting. By providing both a high-quality dataset and a systematic benchmark, we hope that NaijaS2ST will serve as a strong foundation for advancing research in low-resource, multilingual speech translation.

2026-04-16

arXiv (prépublication)

Testing the Assumptions of Active Learning for Translation Tasks with Few Samples

Lorenzo Flores

Cesare Spinoso di-Piano

Ori Ernst

Jackie Chi Kit Cheung

Active learning (AL) is a training paradigm for selecting unlabeled samples for annotation to improve model performance on a test set, which… (voir plus) is useful when only a limited number of samples can be annotated. These algorithms often work by optimizing for the informativeness and diversity of the training data to be annotated. Recent work found that AL strategies fail to outperform random sampling on various language generation tasks when using 100-500 samples. To understand AL's poor performance when only using few samples, we investigate whether the core assumptions underlying AL strategies hold. We find that neither the informativeness nor diversity of the training data, which AL strategies optimize for, are correlated with test set performance. Instead, factors like the ordering of the training samples and interactions with pre-training data have a larger impact on performance. This suggests that future AL methods must take these factors into account in order to work with very few samples.

2026-04-09

arXiv (prépublication)

YoNER: A New Yorùbá Multi-domain Named Entity Recognition Dataset

Peace Busola Falola

Jesujoba O. Alabi

Solomon O. Akinola

Folashade T. Ogunajo

Emmanuel Oluwadunsin Alabi

Named Entity Recognition (NER) is a foundational NLP task, yet research in Yor\`ub\'a has been constrained by limited and domain-specific re… (voir plus)sources. Existing resources, such as MasakhaNER (a manually annotated news-domain corpus) and WikiAnn (automatically created from Wikipedia), are valuable but restricted in domain coverage. To address this gap, we present YoNER, a new multidomain Yor\`ub\'a NER dataset that extends entity coverage beyond news and Wikipedia. The dataset comprises about 5,000 sentences and 100,000 tokens collected from five domains including Bible, Blogs, Movies, Radio broadcast and Wikipedia, and annotated with three entity types: Person (PER), Organization (ORG) and Location (LOC), following CoNLL-style guidelines. Annotation was conducted manually by three native Yor\`ub\'a speakers, with an inter-annotator agreement of over 0.70, ensuring high quality and consistency. We benchmark several transformer encoder models using cross-domain experiments with MasakhaNER 2.0, and we also assess the effect of few-shot in-domain data using YoNER and cross-lingual setups with English datasets. Our results show that African-centric models outperform general multilingual models for Yor\`ub\'a, but cross-domain performance drops substantially, particularly for blogs and movie domains. Furthermore, we observed that closely related formal domains, such as news and Wikipedia, transfer more effectively. In addition, we introduce a new Yor\`ub\'a-specific language model (OyoBERT) that outperforms multilingual models in in-domain evaluation. We publicly release the YoNER dataset and pretrained OyoBERT models to support future research on Yor\`ub\'a natural language processing.

2026-04-06

arXiv (prépublication)

AfrIFact: Cultural Information Retrieval, Evidence Extraction and Fact Checking for African Languages

Israel Abebe Azime

Jesujoba O. Alabi

Crystina Zhang

Iffat Maab

Atnafu Lambebo Tonja

Tadesse Destaw Belay

Folasade Alabi

Salomey Osei

Saminu Muhammad Aliyu

Nkechinyere Faith Aguobi

Bontu Fufa Balcha

Blessing Sibanda

Davis David

Mouhamadane Mboup

Daud Abolade

Neo Putini

Philipp Slusallek

Dietrich Klakow

Assessing the veracity of a claim made online is a complex and important task with real-world implications. When these claims are directed a… (voir plus)t communities with limited access to information and the content concerns issues such as healthcare and culture, the consequences intensify, especially in low-resource languages. In this work, we introduce AfrIFact, a dataset that covers the necessary steps for automatic fact-checking (i.e., information retrieval, evidence extraction, and fact checking), in ten African languages and English. Our evaluation results show that even the best embedding models lack cross-lingual retrieval capabilities, and that cultural and news documents are easier to retrieve than healthcare-domain documents, both in large corpora and in single documents. We show that LLMs lack robust multilingual fact-verification capabilities in African languages, while few-shot prompting improves performance by up to 43% in AfriqueQwen-14B, and task-specific fine-tuning further improves fact-checking accuracy by up to 26%. These findings, along with our release of the AfrIFact dataset, encourage work on low-resource information retrieval, evidence retrieval, and fact checking.

2026-03-31

arXiv (prépublication)