Zobrazeno 1 - 10
of 21
pro vyhledávání: '"Alabi, Jesujoba O."'
Autor:
Adelani, David Ifeoluwa, Ojo, Jessica, Azime, Israel Abebe, Zhuang, Jian Yun, Alabi, Jesujoba O., He, Xuanli, Ochieng, Millicent, Hooker, Sara, Bukula, Andiswa, Lee, En-Shiun Annie, Chukwuneke, Chiamaka, Buzaaba, Happy, Sibanda, Blessing, Kalipe, Godson, Mukiibi, Jonathan, Kabongo, Salomon, Yuehgoh, Foutse, Setaka, Mmasibidi, Ndolela, Lolwethu, Odu, Nkiruka, Mabuya, Rooweither, Muhammad, Shamsuddeen Hassan, Osei, Salomey, Samb, Sokhar, Guge, Tadesse Kebede, Stenetorp, Pontus
Despite the widespread adoption of Large language models (LLMs), their remarkable capabilities remain limited to a few high-resource languages. Additionally, many low-resource languages (e.g. African languages) are often evaluated only on basic text
Externí odkaz:
http://arxiv.org/abs/2406.03368
Autor:
Ilevbare, Comfort Eseohen, Alabi, Jesujoba O., Adelani, David Ifeoluwa, Bakare, Firdous Damilola, Abiola, Oluwatoyin Bunmi, Adeyemo, Oluwaseyi Adesina
Nigerians have a notable online presence and actively discuss political and topical matters. This was particularly evident throughout the 2023 general election, where Twitter was used for campaigning, fact-checking and verification, and even positive
Externí odkaz:
http://arxiv.org/abs/2404.18180
This paper presents our system developed for the SemEval-2024 Task 1: Semantic Textual Relatedness for African and Asian Languages. The shared task aims at measuring the semantic textual relatedness between pairs of sentences, with a focus on a range
Externí odkaz:
http://arxiv.org/abs/2404.01490
We analyze the operation of transformer language adapters, which are small modules trained on top of a frozen language model to adapt its predictions to new target languages. We show that adapted predictions mostly evolve in the source language the m
Externí odkaz:
http://arxiv.org/abs/2402.13137
Autor:
Zhang, Miaoran, Gautam, Vagrant, Wang, Mingyang, Alabi, Jesujoba O., Shen, Xiaoyu, Klakow, Dietrich, Mosbach, Marius
In-context learning is a popular inference strategy where large language models solve a task using only a few labeled demonstrations without needing any parameter updates. Although there have been extensive studies on English in-context learning, mul
Externí odkaz:
http://arxiv.org/abs/2402.12976
Autor:
Adelani, David Ifeoluwa, Liu, Hannah, Shen, Xiaoyu, Vassilyev, Nikita, Alabi, Jesujoba O., Mao, Yanke, Gao, Haonan, Lee, Annie En-Shiun
Despite the progress we have recorded in the last few years in multilingual natural language processing, evaluation is typically limited to a small set of languages with available datasets which excludes a large number of low-resource languages. In t
Externí odkaz:
http://arxiv.org/abs/2309.07445
Autor:
Aremu, Anuoluwapo, Alabi, Jesujoba O., Abolade, Daud, Aguobi, Nkechinyere F., Muhammad, Shamsuddeen Hassan, Adelani, David Ifeoluwa
In this paper, we create NaijaRC: a new multi-choice Reading Comprehension dataset for three native Nigeria languages that is based on high-school reading comprehension examination. We provide baseline results by performing cross-lingual transfer usi
Externí odkaz:
http://arxiv.org/abs/2308.09768
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, Neubig, Graham, Ruder, Sebastian, Rijhwani, Shruti, Beukman, Michael, Palen-Michel, Chester, Lignos, Constantine, Alabi, Jesujoba O., Muhammad, Shamsuddeen H., Nabende, Peter, Dione, Cheikh M. Bamba, Bukula, Andiswa, Mabuya, Rooweither, Dossou, Bonaventure F. P., Sibanda, Blessing, Buzaaba, Happy, Mukiibi, Jonathan, Kalipe, Godson, Mbaye, Derguene, Taylor, Amelia, Kabore, Fatoumata, Emezue, Chris Chinenye, Aremu, Anuoluwapo, Ogayo, Perez, Gitau, Catherine, Munkoh-Buabeng, Edwin, Koagne, Victoire M., Tapo, Allahsera Auguste, Macucwa, Tebogo, Marivate, Vukosi, Mboning, Elvis, Gwadabe, Tajuddeen, Adewumi, Tosin, Ahia, Orevaoghene, Nakatumba-Nabende, Joyce, Mokono, Neo L., Ezeani, Ignatius, Chukwuneke, Chiamaka, Adeyemi, Mofetoluwa, Hacheme, Gilles Q., Abdulmumin, Idris, Ogundepo, Odunayo, Yousuf, Oreen, Ngoli, Tatiana Moteu, Klakow, Dietrich
African languages are spoken by over a billion people, but are underrepresented in NLP research and development. The challenges impeding progress include the limited availability of annotated datasets, as well as a lack of understanding of the settin
Externí odkaz:
http://arxiv.org/abs/2210.12391
Autor:
Abdulmumin, Idris, Beukman, Michael, Alabi, Jesujoba O., Emezue, Chris, Asiko, Everlyn, Adewumi, Tosin, Muhammad, Shamsuddeen Hassan, Adeyemi, Mofetoluwa, Yousuf, Oreen, Singh, Sahib, Gwadabe, Tajuddeen Rabiu
We participated in the WMT 2022 Large-Scale Machine Translation Evaluation for the African Languages Shared Task. This work describes our approach, which is based on filtering the given noisy data using a sentence-pair classifier that was built by fi
Externí odkaz:
http://arxiv.org/abs/2210.10692