Papers by Abigail Oppong
AfriHate: A Multilingual Collection of Hate Speech and Abusive Language Datasets for African Languages (2025.naacl-long)
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Shamsuddeen Hassan Muhammad, Idris Abdulmumin, Abinew Ali Ayele, David Ifeoluwa Adelani, Ibrahim Said Ahmad, Saminu Mohammad Aliyu, Paul Röttger, Abigail Oppong, Andiswa Bukula, Chiamaka Ijeoma Chukwuneke, Ebrahim Chekol Jibril, Elyas Abdi Ismail, Esubalew Alemneh, Hagos Tesfahun Gebremichael, Lukman Jibril Aliyu, Meriem Beloucif, Oumaima Hourrane, Rooweither Mabuya, Salomey Osei, Samuel Rutunda, Tadesse Destaw Belay, Tadesse Kebede Guge, Tesfa Tegegne Asfaw, Lilian Diana Awuor Wanzare, Nelson Odhiambo Onyango, Seid Muhie Yimam, Nedjma Ousidhoum
| Challenge: | Hate speech and abusive language are global phenomena that need sociocultural background knowledge to be understood, identified, and moderated. |
| Approach: | They propose to use a multilingual dataset to collect hate speech and abusive language in 15 African languages to help improve model performance. |
| Outcome: | The proposed datasets are based on tweets annotated by native speakers familiar with the regional culture and show that they perform well in low-resource settings. |
Afri-MCQA: Multimodal Cultural Question Answering for African Languages (2026.acl-long)
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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 A Etori, Eric Peter Wairagala, Kanda Patrick Tshinu, Imanigirimbabazi Emmanuel, Gabofetswe Malema, Alham Fikri Aji, David Ifeoluwa Adelani, Thamar Solorio
| Challenge: | Afri-MCQA is the first multilingual cultural question-answering benchmark containing 7.5k Q A pairs across 15 African languages from 12 countries. |
| Approach: | They introduce Afri-MCQA, the first multilingual cultural question-answering benchmark containing 7.5k Q A pairs across 15 African languages from 12 countries. |
| Outcome: | The proposed model shows poor performance across cultures, with near zero accuracy on open-ended VQA when queried through native language or speech. |