Papers by Abraham Owodunni
Better Quality Pre-training Data and T5 Models for African Languages (2023.emnlp-main)
Copied to clipboard
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. |
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. |
Bloom Library: Multimodal Datasets in 300+ Languages for a Variety of Downstream Tasks (2022.emnlp-main)
Copied to clipboard
| Challenge: | In total, the initial release of the Bloom Library datasets covers 363 languages across 32 language families. |
| Approach: | They present a set of multimodal and multilingual datasets for language modeling, image captioning, visual storytelling, and speech synthesis/recognition. |
| Outcome: | The Bloom Library datasets cover 363 languages across 32 language families. |
AccentFold: A Journey through African Accents for Zero-Shot ASR Adaptation to Target Accents (2024.findings-eacl)
Copied to clipboard
| Challenge: | AccentFold uses spatial relationships to improve speech recognition for accented speech . existing methods for accent recognition have been limited due to data scarcity and budget constraints . |
| Approach: | They propose a method that exploits spatial relationships between learned accent embeddings to improve downstream automatic speech recognition. |
| Outcome: | The proposed method outperforms baseline methods in accented speech training. |