David Ifeoluwa Adelani

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

Maîtrise recherche - McGill

Virginia Ceccatelli

Stagiaire de recherche - McGill

Doctorat - McGill

Yejin Jeon

Postdoctorat - McGill

Gagan Jhajj

Stagiaire de recherche - McGill

Senyu Li Li

Doctorat - McGill

Yinhan Lu

Stagiaire de recherche - McGill

Doctorat - McGill

Damaris Stephanie NDJEBAYI

Doctorat - McGill

Juhyun Oh

Stagiaire de recherche - McGill

Jessica Ojo

Maîtrise recherche - McGill

Kosei Uemura

Stagiaire de recherche - McGill

Vivek Verma

Maîtrise professionnelle - UdeM

Tianyi Xu Xu

Stagiaire de recherche - McGill

Peter Yu

Maîtrise recherche - McGill

Publications

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)

Sudanese-Flores: Extending FLORES+ to Sudanese Arabic Dialect

Hadia Mohmmedosman Ahmed Samil

In this work, we introduce Sudanese-Flores, an extension of the popular Flores+ machine translation (MT) benchmark to the Sudanese Arabic di… (voir plus)alect. We translate both the DEV and DEVTEST splits of the Modern Standard Arabic dataset into the corresponding Sudanese dialect, resulting in a total of 2,009 sentences. While the dialect was recently introduced in Google Translate, there are no available benchmark in this dialect despite spoken by over 40 million people. Our evaluation on two leading LLMs such as GPT-4.1 and Gemini 2.5 Flash showed that while the performance English to Arabic is impressive (more than 23 BLEU), they struggle on Sudanese dialect (less than 11 BLEU) in zero-shot settings. In few-shot scenario, we achieved only a slight boost in performance.

2026-01-26

AfricaNLP @ Conference of European Chapter of Association for Computational Linguistics (publié)

openreview.net

Multilinguality as Sense Adaptation

Jan Christian Blaise Cruz

Alham Fikri Aji

2026-01-14

ArXiv (prépublication)

Afri-MCQA: Multimodal Cultural Question Answering for African Languages

Atnafu Lambebo Tonja

Srija Anand

Emilio Villa Cueva

Israel Abebe Azime

Jesujoba Oluwadara Alabi

Muhidin A. Mohamed

Debela Desalegn Yadeta

Negasi Haile Abadi

Abigail Oppong

Nnaemeka Casmir Obiefuna

Idris Abdulmumin

Naome Etori

Eric Peter Wairagala

Kanda Patrick Tshinu

Imanigirimbabazi Emmanuel

Gabofetswe Malema

Alham Fikri Aji

Thamar Solorio

Africa is home to over one-third of the world's languages, yet remains underrepresented in AI research. We introduce Afri-MCQA, the first Mu… (voir plus)ltilingual Cultural Question-Answering benchmark covering 7.5k Q&A pairs across 15 African languages from 12 countries. The benchmark offers parallel English-African language Q&A pairs across text and speech modalities and was entirely created by native speakers. Benchmarking large language models (LLMs) on Afri-MCQA shows that open-weight models perform poorly across evaluated cultures, with near-zero accuracy on open-ended VQA when queried in native language or speech. To evaluate linguistic competence, we include control experiments meant to assess this specific aspect separate from cultural knowledge, and we observe significant performance gaps between native languages and English for both text and speech. These findings underscore the need for speech-first approaches, culturally grounded pretraining, and cross-lingual cultural transfer. To support more inclusive multimodal AI development in African languages, we release our Afri-MCQA under academic license or CC BY-NC 4.0 on HuggingFace (https://huggingface.co/datasets/Atnafu/Afri-MCQA)

2026-01-08

ArXiv (prépublication)

AfriqueLLM: How Data Mixing and Model Architecture Impact Continued Pre-training for African Languages

Hao Yu

Tianyi Xu

Michael A. Hedderich

Wassim Hamidouche

Syed Waqas Zamir

2025-12-31

arXiv (publié)

Ibom NLP: A Step Toward Inclusive Natural Language Processing for Nigeria's Minority Languages

Oluwadara Kalejaiye

Luel Hagos Beyene

Mmekut-Mfon Gabriel Edet

A. D. Akpan

Eno-Abasi Urua

Anietie U Andy

2025-11-08

ArXiv (prépublication)

Evaluating WMT 2025 Metrics Shared Task Submissions on the SSA-MTE African Challenge Set

Senyu Li

Felermino Dario Mario Ali

Jiayi Wang

Rui Sousa-Silva

Henrique Lopes Cardoso

Pontus Stenetorp

Colin Cherry

2025-10-31

Proceedings of the Tenth Conference on Machine Translation (publié)

Findings of the WMT25 Shared Task on Automated Translation Evaluation Systems: Linguistic Diversity is Challenging and References Still Help

Alon Lavie

Greg Hanneman

Sweta Agrawal

Diptesh Kanojia

Chi-kiu Lo

Vilém Zouhar

Frédéric Blain

Chrysoula Zerva

Eleftherios Avramidis

Sourabh Dattatray Deoghare

Archchana Sindhujan

Jiayi Wang

Brian Thompson

Tom Kocmi

Markus Freitag

Daniel Deutsch

2025-10-31

Proceedings of the Tenth Conference on Machine Translation (publié)

AfriMTEB and AfriE5: Benchmarking and Adapting Text Embedding Models for African Languages

Kosei Uemura

Miaoran Zhang

2025-10-26

ArXiv (prépublication)

DIVERS-Bench: Evaluating Language Identification Across Domain Shifts and Code-Switching

Jessica Ojo

Zina Kamel

2025-08-31

arXiv (publié)

Fleurs-SLU: A Massively Multilingual Benchmark for Spoken Language Understanding

Fabian David Schmidt

Ivan Vulić

Goran Glavaš

Spoken language understanding (SLU) is indispensable for half of all living languages that lack a formal writing system, since these languag… (voir plus)es cannot pair automatic speech recognition (ASR) with language models to benefit from language technology. Even if low-resource languages possess a writing system, ASR for these languages remains unreliable due to limited bimodal speech and text training data. Better SLU can strengthen the robustness of massively multilingual ASR by levering language semantics to disambiguate utterances via context or exploiting semantic similarities across languages. However, the evaluation of multilingual SLU remains limited to shallow tasks such as intent classification or language identification. To address this, we present Fleurs-SLU, a multilingual SLU benchmark that encompasses (i) 692 hours of speech for topical utterance classification in 102 languages and (ii) multiple-choice question answering through listening comprehension spanning 944 hours of speech across 92 languages. We extensively evaluate both end-to-end speech classification models and cascaded systems that combine speech-to-text transcription with subsequent classification by large language models on Fleurs-SLU. Our results show that cascaded systems exhibit greater robustness in multilingual SLU tasks, though speech encoders can achieve competitive performance in topical speech classification when appropriately pre-trained. We further find a strong correlation between robust multilingual ASR, effective speech-to-text translation, and strong multilingual SLU, highlighting the mutual benefits between acoustic and semantic speech representations.

2025-07-06

colmweb.org/COLM/2025/Conference (accepté)

openreview.net

AfroBench: How Good are Large Language Models on African Languages?

Jessica Ojo

Kelechi Ogueji

Pontus Stenetorp

2025-06-30

Findings of the Association for Computational Linguistics: ACL 2025 (publié)