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Tajuddeen Gwadabe

5 accepted papers

2023

AfriSenti: A Twitter Sentiment Analysis Benchmark for African Languages

Shamsuddeen Hassan Muhammad, Idris Abdulmumin, Abinew Ali Ayele, Nedjma OUSIDHOUM, David Ifeoluwa Adelani, Seid Muhie Yimam +21
EMNLP 2023long main

Africa is home to over 2,000 languages from over six language families and has the highest linguistic diversity among all continents. This includes 75 languages with at least one million speakers each. Yet, there is little NLP research conducted on African languages. Crucial in enabling such researc…

Cited by 0Source ↗code ↗Scholar ↗
2023

Cross-lingual Open-Retrieval Question Answering for African Languages

Odunayo Ogundepo, Tajuddeen Gwadabe, Clara E. Rivera, Jonathan H. Clark, Sebastian Ruder, David Ifeoluwa Adelani +38
EMNLP 2023long findings

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 retrieve answer content from other languages while s…

Cited by 0Source ↗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 ↗