Zobrazeno 1 - 10
of 67
pro vyhledávání: '"Dossou, Bonaventure"'
Large Language Models (LLMs) offer extensive knowledge across various domains, but they may inadvertently memorize sensitive, unauthorized, or malicious data, such as personal information in the medical and financial sectors. Machine unlearning metho
Externí odkaz:
http://arxiv.org/abs/2410.10866
Autor:
Tonja, Atnafu Lambebo, Dossou, Bonaventure F. P., Ojo, Jessica, Rajab, Jenalea, Thior, Fadel, Wairagala, Eric Peter, Aremu, Anuoluwapo, Moiloa, Pelonomi, Abbott, Jade, Marivate, Vukosi, Rosman, Benjamin
High-resource language models often fall short in the African context, where there is a critical need for models that are efficient, accessible, and locally relevant, even amidst significant computing and data constraints. This paper introduces Inkub
Externí odkaz:
http://arxiv.org/abs/2408.17024
Autor:
Dossou, Bonaventure F. P.
The Deep Learning revolution has enabled groundbreaking achievements in recent years. From breast cancer detection to protein folding, deep learning algorithms have been at the core of very important advancements. However, these modern advancements a
Externí odkaz:
http://arxiv.org/abs/2401.15721
Autor:
Olatunji, Tobi, Afonja, Tejumade, Yadavalli, Aditya, Emezue, Chris Chinenye, Singh, Sahib, Dossou, Bonaventure F. P., Osuchukwu, Joanne, Osei, Salomey, Tonja, Atnafu Lambebo, Etori, Naome, Mbataku, Clinton
Africa has a very low doctor-to-patient ratio. At very busy clinics, doctors could see 30+ patients per day -- a heavy patient burden compared with developed countries -- but productivity tools such as clinical automatic speech recognition (ASR) are
Externí odkaz:
http://arxiv.org/abs/2310.00274
The Fon language, spoken by an average 2 million of people, is a truly low-resourced African language, with a limited online presence, and existing datasets (just to name but a few). Multitask learning is a learning paradigm that aims to improve the
Externí odkaz:
http://arxiv.org/abs/2308.14280
Autor:
Dossou, Bonaventure F. P.
Accents play a pivotal role in shaping human communication, enhancing our ability to convey and comprehend messages with clarity and cultural nuance. While there has been significant progress in Automatic Speech Recognition (ASR), African-accented En
Externí odkaz:
http://arxiv.org/abs/2306.02105
Autor:
Olatunji, Tobi, Afonja, Tejumade, Dossou, Bonaventure F. P., Tonja, Atnafu Lambebo, Emezue, Chris Chinenye, Rufai, Amina Mardiyyah, Singh, Sahib
Useful conversational agents must accurately capture named entities to minimize error for downstream tasks, for example, asking a voice assistant to play a track from a certain artist, initiating navigation to a specific location, or documenting a la
Externí odkaz:
http://arxiv.org/abs/2306.00253
Autor:
Dione, Cheikh M. Bamba, Adelani, David, Nabende, Peter, Alabi, Jesujoba, Sindane, Thapelo, Buzaaba, Happy, Muhammad, Shamsuddeen Hassan, Emezue, Chris Chinenye, Ogayo, Perez, Aremu, Anuoluwapo, Gitau, Catherine, Mbaye, Derguene, Mukiibi, Jonathan, Sibanda, Blessing, Dossou, Bonaventure F. P., Bukula, Andiswa, Mabuya, Rooweither, Tapo, Allahsera Auguste, Munkoh-Buabeng, Edwin, Koagne, victoire Memdjokam, Kabore, Fatoumata Ouoba, Taylor, Amelia, Kalipe, Godson, Macucwa, Tebogo, Marivate, Vukosi, Gwadabe, Tajuddeen, Elvis, Mboning Tchiaze, Onyenwe, Ikechukwu, Atindogbe, Gratien, Adelani, Tolulope, Akinade, Idris, Samuel, Olanrewaju, Nahimana, Marien, Musabeyezu, Théogène, Niyomutabazi, Emile, Chimhenga, Ester, Gotosa, Kudzai, Mizha, Patrick, Agbolo, Apelete, Traore, Seydou, Uchechukwu, Chinedu, Yusuf, Aliyu, Abdullahi, Muhammad, Klakow, Dietrich
In this paper, we present MasakhaPOS, the largest part-of-speech (POS) dataset for 20 typologically diverse African languages. We discuss the challenges in annotating POS for these languages using the UD (universal dependencies) guidelines. We conduc
Externí odkaz:
http://arxiv.org/abs/2305.13989
Autor:
Ogundepo, Odunayo, Gwadabe, Tajuddeen R., Rivera, Clara E., Clark, Jonathan H., Ruder, Sebastian, Adelani, David Ifeoluwa, Dossou, Bonaventure F. P., DIOP, Abdou Aziz, Sikasote, Claytone, Hacheme, Gilles, Buzaaba, Happy, Ezeani, Ignatius, Mabuya, Rooweither, Osei, Salomey, Emezue, Chris, Kahira, Albert Njoroge, Muhammad, Shamsuddeen H., Oladipo, Akintunde, Owodunni, Abraham Toluwase, Tonja, Atnafu Lambebo, Shode, Iyanuoluwa, Asai, Akari, Ajayi, Tunde Oluwaseyi, Siro, Clemencia, Arthur, Steven, Adeyemi, Mofetoluwa, Ahia, Orevaoghene, Aremu, Anuoluwapo, Awosan, Oyinkansola, Chukwuneke, Chiamaka, Opoku, Bernard, Ayodele, Awokoya, Otiende, Verrah, Mwase, Christine, Sinkala, Boyd, Rubungo, Andre Niyongabo, Ajisafe, Daniel A., Onwuegbuzia, Emeka Felix, Mbow, Habib, Niyomutabazi, Emile, Mukonde, Eunice, Lawan, Falalu Ibrahim, Ahmad, Ibrahim Said, Alabi, Jesujoba O., Namukombo, Martin, Chinedu, Mbonu, Phiri, Mofya, Putini, Neo, Mngoma, Ndumiso, Amuok, Priscilla A., Iro, Ruqayya Nasir, Adhiambo, Sonia
African languages have far less in-language content available digitally, making it challenging for question answering systems to satisfy the information needs of users. Cross-lingual open-retrieval question answering (XOR QA) systems -- those that re
Externí odkaz:
http://arxiv.org/abs/2305.06897
Autor:
Adelani, David Ifeoluwa, Masiak, Marek, Azime, Israel Abebe, Alabi, Jesujoba, Tonja, Atnafu Lambebo, Mwase, Christine, Ogundepo, Odunayo, Dossou, Bonaventure F. P., Oladipo, Akintunde, Nixdorf, Doreen, Emezue, Chris Chinenye, al-azzawi, sana, Sibanda, Blessing, David, Davis, Ndolela, Lolwethu, Mukiibi, Jonathan, Ajayi, Tunde, Moteu, Tatiana, Odhiambo, Brian, Owodunni, Abraham, Obiefuna, Nnaemeka, Mohamed, Muhidin, Muhammad, Shamsuddeen Hassan, Ababu, Teshome Mulugeta, Salahudeen, Saheed Abdullahi, Yigezu, Mesay Gemeda, Gwadabe, Tajuddeen, Abdulmumin, Idris, Taye, Mahlet, Awoyomi, Oluwabusayo, Shode, Iyanuoluwa, Adelani, Tolulope, Abdulganiyu, Habiba, Omotayo, Abdul-Hakeem, Adeeko, Adetola, Afolabi, Abeeb, Aremu, Anuoluwapo, Samuel, Olanrewaju, Siro, Clemencia, Kimotho, Wangari, Ogbu, Onyekachi, Mbonu, Chinedu, Chukwuneke, Chiamaka, Fanijo, Samuel, Ojo, Jessica, Awosan, Oyinkansola, Kebede, Tadesse, Sakayo, Toadoum Sari, Nyatsine, Pamela, Sidume, Freedmore, Yousuf, Oreen, Oduwole, Mardiyyah, Tshinu, Tshinu, Kimanuka, Ussen, Diko, Thina, Nxakama, Siyanda, Nigusse, Sinodos, Johar, Abdulmejid, Mohamed, Shafie, Hassan, Fuad Mire, Mehamed, Moges Ahmed, Ngabire, Evrard, Jules, Jules, Ssenkungu, Ivan, Stenetorp, Pontus
African languages are severely under-represented in NLP research due to lack of datasets covering several NLP tasks. While there are individual language specific datasets that are being expanded to different tasks, only a handful of NLP tasks (e.g. n
Externí odkaz:
http://arxiv.org/abs/2304.09972