Zobrazeno 1 - 10
of 23
pro vyhledávání: '"Ogayo, Perez"'
Autor:
Wang, Jiayi, Adelani, David Ifeoluwa, Agrawal, Sweta, Masiak, Marek, Rei, Ricardo, Briakou, Eleftheria, Carpuat, Marine, He, Xuanli, Bourhim, Sofia, Bukula, Andiswa, Mohamed, Muhidin, Olatoye, Temitayo, Adewumi, Tosin, Mokayed, Hamam, Mwase, Christine, Kimotho, Wangui, Yuehgoh, Foutse, Aremu, Anuoluwapo, Ojo, Jessica, Muhammad, Shamsuddeen Hassan, Osei, Salomey, Omotayo, Abdul-Hakeem, Chukwuneke, Chiamaka, Ogayo, Perez, Hourrane, Oumaima, Anigri, Salma El, Ndolela, Lolwethu, Mangwana, Thabiso, Mohamed, Shafie Abdi, Hassan, Ayinde, Awoyomi, Oluwabusayo Olufunke, Alkhaled, Lama, Al-Azzawi, Sana, Etori, Naome A., Ochieng, Millicent, Siro, Clemencia, Njoroge, Samuel, Muchiri, Eric, Kimotho, Wangari, Momo, Lyse Naomi Wamba, Abolade, Daud, Ajao, Simbiat, Shode, Iyanuoluwa, Macharm, Ricky, Iro, Ruqayya Nasir, Abdullahi, Saheed S., Moore, Stephen E., Opoku, Bernard, Akinjobi, Zainab, Afolabi, Abeeb, Obiefuna, Nnaemeka, Ogbu, Onyekachi Raphael, Brian, Sam, Otiende, Verrah Akinyi, Mbonu, Chinedu Emmanuel, Sari, Sakayo Toadoum, Lu, Yao, Stenetorp, Pontus
Despite the recent progress on scaling multilingual machine translation (MT) to several under-resourced African languages, accurately measuring this progress remains challenging, since evaluation is often performed on n-gram matching metrics such as
Externí odkaz:
http://arxiv.org/abs/2311.09828
Large language models (LLMs) implicitly learn to perform a range of language tasks, including machine translation (MT). Previous studies explore aspects of LLMs' MT capabilities. However, there exist a wide variety of languages for which recent LLM M
Externí odkaz:
http://arxiv.org/abs/2309.07423
Autor:
Kabra, Anubha, Liu, Emmy, Khanuja, Simran, Aji, Alham Fikri, Winata, Genta Indra, Cahyawijaya, Samuel, Aremu, Anuoluwapo, Ogayo, Perez, Neubig, Graham
Figurative language permeates human communication, but at the same time is relatively understudied in NLP. Datasets have been created in English to accelerate progress towards measuring and improving figurative language processing in language models
Externí odkaz:
http://arxiv.org/abs/2305.16171
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:
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
Developing Automatic Speech Recognition (ASR) for low-resource languages is a challenge due to the small amount of transcribed audio data. For many such languages, audio and text are available separately, but not audio with transcriptions. Using text
Externí odkaz:
http://arxiv.org/abs/2207.09889
Autor:
Meyer, Josh, Adelani, David Ifeoluwa, Casanova, Edresson, Öktem, Alp, Weber, Daniel Whitenack Julian, Kabongo, Salomon, Salesky, Elizabeth, Orife, Iroro, Leong, Colin, Ogayo, Perez, Emezue, Chris, Mukiibi, Jonathan, Osei, Salomey, Agbolo, Apelete, Akinode, Victor, Opoku, Bernard, Olanrewaju, Samuel, Alabi, Jesujoba, Muhammad, Shamsuddeen
BibleTTS is a large, high-quality, open speech dataset for ten languages spoken in Sub-Saharan Africa. The corpus contains up to 86 hours of aligned, studio quality 48kHz single speaker recordings per language, enabling the development of high-qualit
Externí odkaz:
http://arxiv.org/abs/2207.03546
Modern speech synthesis techniques can produce natural-sounding speech given sufficient high-quality data and compute resources. However, such data is not readily available for many languages. This paper focuses on speech synthesis for low-resourced
Externí odkaz:
http://arxiv.org/abs/2207.00688
Autor:
Adelani, David Ifeoluwa, Alabi, Jesujoba Oluwadara, Fan, Angela, Kreutzer, Julia, Shen, Xiaoyu, Reid, Machel, Ruiter, Dana, Klakow, Dietrich, Nabende, Peter, Chang, Ernie, Gwadabe, Tajuddeen, Sackey, Freshia, Dossou, Bonaventure F. P., Emezue, Chris Chinenye, Leong, Colin, Beukman, Michael, Muhammad, Shamsuddeen Hassan, Jarso, Guyo Dub, Yousuf, Oreen, Rubungo, Andre Niyongabo, Hacheme, Gilles, Wairagala, Eric Peter, Nasir, Muhammad Umair, Ajibade, Benjamin Ayoade, Ajayi, Tunde Oluwaseyi, Gitau, Yvonne Wambui, Abbott, Jade, Ahmed, Mohamed, Ochieng, Millicent, Aremu, Anuoluwapo, Ogayo, Perez, Mukiibi, Jonathan, Kabore, Fatoumata Ouoba, Kalipe, Godson Koffi, Mbaye, Derguene, Tapo, Allahsera Auguste, Koagne, Victoire Memdjokam, Munkoh-Buabeng, Edwin, Wagner, Valencia, Abdulmumin, Idris, Awokoya, Ayodele, Buzaaba, Happy, Sibanda, Blessing, Bukula, Andiswa, Manthalu, Sam
Recent advances in the pre-training of language models leverage large-scale datasets to create multilingual models. However, low-resource languages are mostly left out in these datasets. This is primarily because many widely spoken languages are not
Externí odkaz:
http://arxiv.org/abs/2205.02022
Autor:
Fernandes, Patrick, Farinhas, António, Rei, Ricardo, de Souza, José G. C., Ogayo, Perez, Neubig, Graham, Martins, André F. T.
Despite the progress in machine translation quality estimation and evaluation in the last years, decoding in neural machine translation (NMT) is mostly oblivious to this and centers around finding the most probable translation according to the model
Externí odkaz:
http://arxiv.org/abs/2205.00978