AIConfPaper
← Search

Jonathan Mukiibi

5 accepted papers

2025

INJONGO: A Multicultural Intent Detection and Slot-filling Dataset for 16 African Languages

Hao Yu, Jesujoba Oluwadara Alabi, Andiswa Bukula, Jian Yun Zhuang, En-Shiun Annie Lee, Tadesse Kebede Guge +15
ACL 2025long

Slot-filling and intent detection are well-established tasks in Conversational AI. However, current large-scale benchmarks for these tasks often exclude evaluations of low-resource languages and rely on translations from English benchmarks, thereby predominantly reflecting Western-centric concepts.…

Cited by 0Source ↗PDF ↗Scholar ↗
2025

IrokoBench: A New Benchmark for African Languages in the Age of Large Language Models

David Ifeoluwa Adelani, Jessica Ojo, Israel Abebe Azime, Jian Yun Zhuang, Jesujoba Oluwadara Alabi, Xuanli He +21
NAACL 2025long

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 classification tasks due to the lack of appropriat…

Cited by 7Source ↗PDF ↗code ↗Scholar ↗
2023

MasakhaPOS: Part-of-Speech Tagging for Typologically Diverse African languages

Cheikh M. Bamba Dione, David Ifeoluwa Adelani, Peter Nabende, Jesujoba Alabi, Thapelo Sindane, Happy Buzaaba +38
ACL 2023long

In this paper, we present AfricaPOS, 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 universal dependencies (UD) guidelines. We conducted extensive POS baseline experiments using both c…

Cited by 11Source ↗PDF ↗code ↗Scholar ↗
2022

A Few Thousand Translations Go a Long Way! Leveraging Pre-trained Models for African News Translation

David Ifeoluwa Adelani, Jesujoba Oluwadara Alabi, Angela Fan, Julia Kreutzer, Xiaoyu Shen, Machel Reid +39
NAACL 2022long

Recent advances in the pre-training for 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 that are not well represented on the web and therefore ex…

Cited by 54Source ↗PDF ↗code ↗Scholar ↗
2022

MasakhaNER 2.0: Africa-centric Transfer Learning for Named Entity Recognition

David Ifeoluwa Adelani, Graham Neubig, Sebastian Ruder, Shruti Rijhwani, Michael Beukman, Chester Palen-Michel +39
EMNLP 2022main

African languages are spoken by over a billion people, but they are under-represented in NLP research and development. Multiple challenges exist, including the limited availability of annotated training and evaluation datasets as well as the lack of understanding of which settings, languages, and re…

Cited by 31Source ↗PDF ↗code ↗Scholar ↗