Papers by Chiamaka Chukwuneke
MasakhaNER: Named Entity Recognition for African Languages (2021.tacl-1)
Copied to clipboard
David Ifeoluwa Adelani, Jade Abbott, Graham Neubig, Daniel D’souza, Julia Kreutzer, Constantine Lignos, Chester Palen-Michel, Happy Buzaaba, Shruti Rijhwani, Sebastian Ruder, Stephen Mayhew, Israel Abebe Azime, Shamsuddeen H. Muhammad, Chris Chinenye Emezue, Joyce Nakatumba-Nabende, Perez Ogayo, Aremu Anuoluwapo, Catherine Gitau, Derguene Mbaye, Jesujoba Alabi, Seid Muhie Yimam, Tajuddeen Rabiu Gwadabe, Ignatius Ezeani, Rubungo Andre Niyongabo, Jonathan Mukiibi, Verrah Otiende, Iroro Orife, Davis David, Samba Ngom, Tosin Adewumi, Paul Rayson, Mofetoluwa Adeyemi, Gerald Muriuki, Emmanuel Anebi, Chiamaka Chukwuneke, Nkiruka Odu, Eric Peter Wairagala, Samuel Oyerinde, Clemencia Siro, Tobius Saul Bateesa, Temilola Oloyede, Yvonne Wambui, Victor Akinode, Deborah Nabagereka, Maurice Katusiime, Ayodele Awokoya, Mouhamadane MBOUP, Dibora Gebreyohannes, Henok Tilaye, Kelechi Nwaike, Degaga Wolde, Abdoulaye Faye, Blessing Sibanda, Orevaoghene Ahia, Bonaventure F. P. Dossou, Kelechi Ogueji, Thierno Ibrahima DIOP, Abdoulaye Diallo, Adewale Akinfaderin, Tendai Marengereke, Salomey Osei
| Challenge: | (2020) African languages are underrepresented in existing natural language processing datasets, research, and tools due to lack of datasets and reproducible results. |
| Approach: | They propose to create a dataset for named entity recognition (NER) in ten African languages. |
| Outcome: | The results of the first large dataset for named entity recognition (NER) in ten African languages are released to inform future research on African NLP. |
The IgboAPI Dataset: Empowering Igbo Language Technologies through Multi-dialectal Enrichment (2024.lrec-main)
Copied to clipboard
Chris Chinenye Emezue, Ifeoma Okoh, Chinedu Emmanuel Mbonu, Chiamaka Chukwuneke, Daisy Monika Lal, Ignatius Ezeani, Paul Rayson, Ijemma Onwuzulike, Chukwuma Onyebuchi Okeke, Gerald Okey Nweya, Bright Ikechukwu Ogbonna, Chukwuebuka Uchenna Oraegbunam, Esther Chidinma Awo-Ndubuisi, Akudo Amarachukwu Osuagwu
| Challenge: | UNESCO projects that the Igbo language will be endangered by 2025 . primary obstacle in developing dialectal-aware language technologies is lack of comprehensive dialectal datasets. |
| Approach: | They propose to use a multi-dialectal Igbo-English dictionary dataset to enhance the representation of Igbe dialects. |
| Outcome: | The proposed dataset enables machine translation systems to handle dialect variations in sentences. |
Cross-lingual Open-Retrieval Question Answering for African Languages (2023.findings-emnlp)
Copied to clipboard
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. |
AfriMTE and AfriCOMET: Enhancing COMET to Embrace Under-resourced African Languages (2024.naacl-long)
Copied to clipboard
Jiayi Wang, David Adelani, Sweta Agrawal, Marek Masiak, Ricardo Rei, Eleftheria Briakou, Marine Carpuat, Xuanli He, Sofia Bourhim, Andiswa Bukula, Muhidin Mohamed, Temitayo Olatoye, Tosin Adewumi, Hamam Mokayed, Christine Mwase, Wangui Kimotho, Foutse Yuehgoh, Anuoluwapo Aremu, Jessica Ojo, Shamsuddeen Muhammad, Salomey Osei, Abdul-Hakeem Omotayo, Chiamaka Chukwuneke, Perez Ogayo, Oumaima Hourrane, Salma El Anigri, Lolwethu Ndolela, Thabiso Mangwana, Shafie Mohamed, Hassan Ayinde, Oluwabusayo Awoyomi, Lama Alkhaled, Sana Al-azzawi, Naome Etori, Millicent Ochieng, Clemencia Siro, Njoroge Kiragu, Eric Muchiri, Wangari Kimotho, Toadoum Sari Sakayo, Lyse Naomi Wamba, Daud Abolade, Simbiat Ajao, Iyanuoluwa Shode, Ricky Macharm, Ruqayya Iro, Saheed Abdullahi, Stephen Moore, Bernard Opoku, Zainab Akinjobi, Abeeb Afolabi, Nnaemeka Obiefuna, Onyekachi Ogbu, Sam Ochieng’, Verrah Otiende, Chinedu Mbonu, Yao Lu, Pontus Stenetorp
| Challenge: | Recent advances in machine translation (MT) have focused on scaling multilingual machine translation models and evaluation data to hundreds of languages, including multiple under-resourced languages. |
| Approach: | They propose to use n-gram matching metrics to measure progress in multilingual machine translation to 13 typologically diverse African languages to create high-quality human evaluation data with simplified MQM guidelines. |
| Outcome: | The proposed metrics have a higher correlation with human judgments than n-gram matching metrics such as BLEU and METEOR. |
IgboBERT Models: Building and Training Transformer Models for the Igbo Language (2022.lrec-1)
Copied to clipboard
| Challenge: | This paper focuses on building resources for named entity recognition for Igbo, a language mainly spoken in the south eastern part of Nigeria. |
| Approach: | They present a standard Igbo named entity recognition dataset and results from fine-tuning transformer IgbeNER models. |
| Outcome: | The proposed dataset and model improves on the IgboNER task while training and fine-tuning a transformer model with comparatively little Igbe text data. |
MasakhaNER 2.0: Africa-centric Transfer Learning for Named Entity Recognition (2022.emnlp-main)
Copied to clipboard
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 . |