Happy Buzaaba
University of Tsukuba
14 Papers
7 Citations
Happy Buzaaba is an academic researcher from University of Tsukuba. The author has contributed to research in topics: Computer science & Languages of Africa. The author has an hindex of 2, co-authored 4 publications.
Chat about Author
Papers
MasakhaNER: Named Entity Recognition for African Languages
David Ifeoluwa Adelani,Jade Abbott,Graham Neubig,Daniel D'souza,Julia Kreutzer,Constantine Lignos,Chester Palen-Michel,Happy Buzaaba,Shruti Rijhwani,Sebastian Ruder,Stephen Mayhew,Israel Abebe Azime,Shamsuddeen Hassan Muhammad,Shamsuddeen Hassan Muhammad,Chris Chinenye Emezue,Joyce Nakatumba-Nabende,Perez Ogayo,Aremu Anuoluwapo,Catherine Gitau,Derguene Mbaye,Jesujoba O. Alabi,Seid Muhie Yimam,Tajuddeen R. Gwadabe,Ignatius Ezeani,Rubungo Andre Niyongabo,Jonathan Mukiibi,Verrah Otiende,Iroro Orife,Davis David,Samba Ngom,Tosin P. Adewumi,Paul Rayson,Mofetoluwa Adeyemi,Gerald Muriuki,Emmanuel Anebi,Chiamaka Chukwuneke,Nkiruka Odu,Eric Peter Wairagala,Samuel Oyerinde,Clemencia Siro,Tobius Saul Bateesa,Temilola Oloyede,Yvonne Wambui,Victor Akinode,Deborah Nabagereka,Maurice Katusiime,Ayodele Awokoya,Mouhamadane Mboup,Dibora Gebreyohannes,Henok Tilaye,Kelechi Nwaike,Degaga Wolde,Abdoulaye Faye,Blessing Sibanda,Orevaoghene Ahia,Bonaventure F. P. Dossou,Kelechi Ogueji,Thierno Ibrahima Diop,Abdoulaye Diallo,Adewale Akinfaderin,Tendai Marengereke,Salomey Osei +61 more
TL;DR: In this article, the authors present the first large, publicly available, high-quality dataset for named entity recognition (NER) in ten African languages and conduct an extensive empirical evaluation of state-of-the-art methods across both supervised and transfer learning settings.
Question Answering Over Knowledge Base: A Scheme for Integrating Subject and the Identified Relation to Answer Simple Questions
Happy Buzaaba,Toshiyuki Amagasa +1 more
- 01 Feb 2021
TL;DR: This article decompose the question answering problem in a three-step pipeline of entity detection, entity linking, and relation prediction, and solve each component separately, and show that a combination of basic LSTMs, GRUs, and non-neural network techniques achieve reasonable performance.
Ìtàkúròso: Exploiting Cross-Lingual Transferability for Natural Language Generation of Dialogues in Low-Resource, African Languages
Tosin P. Adewumi,Mofe Adeyemi,Aremu Anuoluwapo,Bukola Peters,Happy Buzaaba,Oyerinde Samuel,Amina Mardiyyah Rufai,Benjamin Olusola Ajibade,Tajudeen Gwadabe,M. Traore,Tunde Ajayi,Shamsuddeen Hassan Muhammad,A. D. Baruwa,Paul Owoicho,Tolúlope' Ògúnremí,Phylis Ngigi,Orevaoghene Ahia,Ruqayya Nasir,Foteini Liwicki,Marcus Liwicki +19 more
TL;DR: The results show that the hypothesis that deep monolingual models learn some abstractions that generalise across languages holds, and demon-strating the cross-lingual transferability hypothesis for dialogue systems.
7
A Modular Approach for Efficient Simple Question Answering Over Knowledge Base
Happy Buzaaba,Toshiyuki Amagasa +1 more
- 26 Aug 2019
TL;DR: This work decomposes the simple QA task in a three step-pipeline: entity detection, entity linking and relation prediction, and introduces a novel index that relies on the relation type to filter out subject entities from the candidate list so that the object entity with the highest score becomes the answer to the question.
2
AfriWOZ: Corpus for Exploiting Cross-Lingual Transferability for Generation of Dialogues in Low-Resource, African Languages
Tosin P. Adewumi,Mofe Adeyemi,Aremu Anuoluwapo,Bukola Peters,Happy Buzaaba,Oyerinde Samuel,Amina Mardiyyah Rufai,Benjamin Olusola Ajibade,Tajudeen Gwadabe,M. Traore,Tunde Ajayi,Shamsuddeen Hassan Muhammad,A. D. Baruwa,Paul Owoicho,Tolúlope' Ògúnremí,Phylis Ngigi,Orevaoghene Ahia,Ruqayya Nasir,Foteini Liwicki,Marcus Liwicki +19 more
- 17 Apr 2022
TL;DR: It is shown that the hypothesis that deep monolingual models learn some abstractions that generalize across languages holds, and the language with the most transferable properties is the Nigerian Pidgin English, with a human-likeness score of 78.1%.