Papers by Odunayo Ogundepo
Better Quality Pre-training Data and T5 Models for African Languages (2023.emnlp-main)
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Akintunde Oladipo, Mofetoluwa Adeyemi, Orevaoghene Ahia, Abraham Owodunni, Odunayo Ogundepo, David Adelani, Jimmy Lin
| Challenge: | Existing web crawls have demonstrated quality issues for low-resource languages . Existing pretraining corpora have numerous quality issues . |
| Approach: | They propose to audit existing pretraining corpora to understand and rectify quality issues . they pretrain a new T5-based model and evaluate its performance on multiple tasks . |
| Outcome: | The proposed model outperforms existing pretrained models on four NLP tasks. |
AfriCLIRMatrix: Enabling Cross-Lingual Information Retrieval for African Languages (2022.emnlp-main)
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| Challenge: | Existing datasets for cross-lingual information retrieval are limited in many languages, especially those spoken in Africa. |
| Approach: | They propose to build a test collection for cross-lingual information retrieval in 15 diverse African languages. |
| Outcome: | AfriCLIRMatrix contains 6 million queries in English and 23 million relevance judgments automatically mined from Wikipedia inter-language links, covering many more African languages than any existing information retrieval test collection. |
“Knowing When You Don’t Know”: A Multilingual Relevance Assessment Dataset for Robust Retrieval-Augmented Generation (2024.findings-emnlp)
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Nandan Thakur, Luiz Bonifacio, Crystina Zhang, Odunayo Ogundepo, Ehsan Kamalloo, David Alfonso-Hermelo, Xiaoguang Li, Qun Liu, Boxing Chen, Mehdi Rezagholizadeh, Jimmy Lin
| Challenge: | Prior work on RAG grounds Large Language Models to reduce factual hallucinations lacks a comprehensive evaluation of different language families. |
| Approach: | They propose a human-annotated dataset for evaluating LLM robustness in RAG . they find that most models struggle to balance the two capacities . |
| Outcome: | The proposed dataset includes both a non-relevant and a relevant subset. |
MIRACL: A Multilingual Retrieval Dataset Covering 18 Diverse Languages (2023.tacl-1)
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Xinyu Zhang, Nandan Thakur, Odunayo Ogundepo, Ehsan Kamalloo, David Alfonso-Hermelo, Xiaoguang Li, Qun Liu, Mehdi Rezagholizadeh, Jimmy Lin
| Challenge: | MIRACL is a multilingual dataset for ad hoc retrieval across 18 languages that collectively encompass over three billion native speakers around the world. |
| Approach: | They have gathered over 726k high-quality relevance judgments for 78k queries over Wikipedia in these languages, where all annotations have been performed by native speakers hired by their team. |
| Outcome: | MIRACL covers languages that are typologically close as well as distant from 10 language families and 13 sub-families, associated with varying amounts of publicly available resources. |
Cross-lingual Open-Retrieval Question Answering for African Languages (2023.findings-emnlp)
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Odunayo Ogundepo, Tajuddeen Gwadabe, Clara Rivera, Jonathan Clark, Sebastian Ruder, David Adelani, Bonaventure Dossou, Abdou Diop, Claytone Sikasote, Gilles Hacheme, Happy Buzaaba, Ignatius Ezeani, Rooweither Mabuya, Salomey Osei, Chris Emezue, Albert Kahira, Shamsuddeen Muhammad, Akintunde Oladipo, Abraham Owodunni, Atnafu Tonja, Iyanuoluwa Shode, Akari Asai, Anuoluwapo Aremu, Ayodele Awokoya, Bernard Opoku, Chiamaka Chukwuneke, Christine Mwase, Clemencia Siro, Stephen Arthur, Tunde Ajayi, Verrah Otiende, Andre Rubungo, Boyd Sinkala, Daniel Ajisafe, Emeka Onwuegbuzia, Falalu Lawan, Ibrahim Ahmad, Jesujoba Alabi, Chinedu Mbonu, Mofetoluwa Adeyemi, Mofya Phiri, Orevaoghene Ahia, Ruqayya Iro, Sonia Adhiambo
| Challenge: | Our Dataset is the first cross-lingual QA dataset with a focus on African languages. |
| Approach: | They propose to use African languages as the only high-coverage source of answer content for cross-lingual open-retrieval question answering systems. |
| Outcome: | Our Dataset includes 12,000+ XOR QA examples across 10 African languages. |
Spacerini: Plug-and-play Search Engines with Pyserini and Hugging Face (2023.emnlp-demo)
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Christopher Akiki, Odunayo Ogundepo, Aleksandra Piktus, Xinyu Zhang, Akintunde Oladipo, Jimmy Lin, Martin Potthast
| Challenge: | a toolkit for reproducible information retrieval research is available for free. |
| Approach: | They present a tool that integrates Pyserini and Hugging Face to enable the seamless construction and deployment of interactive search engines. |
| Outcome: | The proposed tool makes state-of-the-art retrieval models more accessible to non-IR practitioners while minimizing deployment effort. |
Evaluating Embedding APIs for Information Retrieval (2023.acl-industry)
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Ehsan Kamalloo, Xinyu Zhang, Odunayo Ogundepo, Nandan Thakur, David Alfonso-hermelo, Mehdi Rezagholizadeh, Jimmy Lin
| Challenge: | a growing number of language models are limiting their access to the community . we evaluate existing APIs for domain generalization and multilingual retrieval . |
| Approach: | They evaluate semantic embedding APIs in retrieval scenarios to assess their capabilities . they use BEIR and MIRACL to re-rank BM25 results using the APIs . |
| Outcome: | The proposed model is based on semantic embedding APIs that build vector representations of a given text. |
MasakhaNER 2.0: Africa-centric Transfer Learning for Named Entity Recognition (2022.emnlp-main)
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David Adelani, Graham Neubig, Sebastian Ruder, Shruti Rijhwani, Michael Beukman, Chester Palen-Michel, Constantine Lignos, Jesujoba Alabi, Shamsuddeen Muhammad, Peter Nabende, Cheikh M. Bamba Dione, Andiswa Bukula, Rooweither Mabuya, Bonaventure F. P. Dossou, Blessing Sibanda, Happy Buzaaba, Jonathan Mukiibi, Godson Kalipe, Derguene Mbaye, Amelia Taylor, Fatoumata Kabore, Chris Chinenye Emezue, Anuoluwapo Aremu, Perez Ogayo, Catherine Gitau, Edwin Munkoh-Buabeng, Victoire Memdjokam Koagne, Allahsera Auguste Tapo, Tebogo Macucwa, Vukosi Marivate, Mboning Tchiaze Elvis, Tajuddeen Gwadabe, Tosin Adewumi, Orevaoghene Ahia, Joyce Nakatumba-Nabende, Neo Lerato Mokono, Ignatius Ezeani, Chiamaka Chukwuneke, Mofetoluwa Oluwaseun Adeyemi, Gilles Quentin Hacheme, Idris Abdulmumin, Odunayo Ogundepo, Oreen Yousuf, Tatiana Moteu, Dietrich Klakow
| Challenge: | Existing studies on named entity recognition methods for African languages focus on English as the source language, but there is evidence that it is not the best for low-resource languages. |
| Approach: | They propose to use human-annotated datasets to analyze named entity recognition tasks in 20 African languages to test whether they are effective. |
| Outcome: | The proposed method improves zero-shot F1 scores by 14% over 20 languages compared to using English . |
GAIA Search: Hugging Face and Pyserini Interoperability for NLP Training Data Exploration (2023.acl-demo)
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Aleksandra Piktus, Odunayo Ogundepo, Christopher Akiki, Akintunde Oladipo, Xinyu Zhang, Hailey Schoelkopf, Stella Biderman, Martin Potthast, Jimmy Lin
| Challenge: | Using the mature and well-tested methods from the domain of Information Retrieval (IR) we propose to integrate Pyserini with Hugging Face to provide qualitative analysis tools for NLP researchers. |
| Approach: | They propose to integrate Pyserini with Hugging Face to provide qualitative analysis tools for NLP researchers. |
| Outcome: | The proposed tools can be integrated with the Hugging Face ecosystem of open-source AI libraries and artifacts. |
AfroBench: How Good are Large Language Models on African Languages? (2025.findings-acl)
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Jessica Ojo, Odunayo Ogundepo, Akintunde Oladipo, Kelechi Ogueji, Jimmy Lin, Pontus Stenetorp, David Ifeoluwa Adelani
| Challenge: | Large-scale multilingual evaluations often include only a handful of African languages due to the scarcity of high-quality data and the limited discoverability of existing datasets. |
| Approach: | They propose a multi-task benchmark to evaluate the performance of LLMs across 64 African languages, 15 tasks and 22 datasets. |
| Outcome: | The proposed benchmark compares LLMs across 64 African languages, 15 tasks and 22 datasets. |