Rooweither Mabuya


2024

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Proceedings of the Fifth Workshop on Resources for African Indigenous Languages @ LREC-COLING 2024
Rooweither Mabuya | Muzi Matfunjwa | Mmasibidi Setaka | Menno van Zaanen
Proceedings of the Fifth Workshop on Resources for African Indigenous Languages @ LREC-COLING 2024

2023

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Proceedings of the Fourth workshop on Resources for African Indigenous Languages (RAIL 2023)
Rooweither Mabuya | Don Mthobela | Mmasibidi Setaka | Menno Van Zaanen
Proceedings of the Fourth workshop on Resources for African Indigenous Languages (RAIL 2023)

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Unsupervised Cross-lingual Word Embedding Representation for English-isiZulu
Derwin Ngomane | Rooweither Mabuya | Jade Abbott | Vukosi Marivate
Proceedings of the Fourth workshop on Resources for African Indigenous Languages (RAIL 2023)

In this study, we investigate the effectiveness of using cross-lingual word embeddings for zero-shot transfer learning between a language with an abundant resource, English, and a languagewith limited resource, isiZulu. IsiZulu is a part of the South African Nguni language family, which is characterised by complex agglutinating morphology. We use VecMap, an open source tool, to obtain cross-lingual word embeddings. To perform an extrinsic evaluation of the effectiveness of the embeddings, we train a news classifier on labelled English data in order to categorise unlabelled isiZulu data using zero-shot transfer learning. In our study, we found our model to have a weighted average F1-score of 0.34. Our findings demonstrate that VecMap generates modular word embeddings in the cross-lingual space that have an impact on the downstream classifier used for zero-shot transfer learning.

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Cross-lingual Open-Retrieval Question Answering for African Languages
Odunayo Ogundepo | Tajuddeen Gwadabe | Clara Rivera | Jonathan Clark | Sebastian Ruder | David Adelani | Bonaventure Dossou | Abdou Diop | Claytone Sikasote | Gilles Hacheme | Happy Buzaaba | Ignatius Ezeani | Rooweither Mabuya | Salomey Osei | Chris Emezue | Albert Kahira | Shamsuddeen Muhammad | Akintunde Oladipo | Abraham Owodunni | Atnafu Tonja | Iyanuoluwa Shode | Akari Asai | Anuoluwapo Aremu | Ayodele Awokoya | Bernard Opoku | Chiamaka Chukwuneke | Christine Mwase | Clemencia Siro | Stephen Arthur | Tunde Ajayi | Verrah Otiende | Andre Rubungo | Boyd Sinkala | Daniel Ajisafe | Emeka Onwuegbuzia | Falalu Lawan | Ibrahim Ahmad | Jesujoba Alabi | Chinedu Mbonu | Mofetoluwa Adeyemi | Mofya Phiri | Orevaoghene Ahia | Ruqayya Iro | Sonia Adhiambo
Findings of the Association for Computational Linguistics: EMNLP 2023

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 serving people in their native language—offer a means of filling this gap. To this end, we create Our Dataset, the first cross-lingual QA dataset with a focus on African languages. Our Dataset includes 12,000+ XOR QA examples across 10 African languages. While previous datasets have focused primarily on languages where cross-lingual QA augments coverage from the target language, Our Dataset focuses on languages where cross-lingual answer content is the only high-coverage source of answer content. Because of this, we argue that African languages are one of the most important and realistic use cases for XOR QA. Our experiments demonstrate the poor performance of automatic translation and multilingual retrieval methods. Overall, Our Dataset proves challenging for state-of-the-art QA models. We hope that the dataset enables the development of more equitable QA technology.

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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 | Shamsuddeen Hassan Muhammad | Chris Chinenye Emezue | Perez Ogayo | Anuoluwapo Aremu | Catherine Gitau | Derguene Mbaye | Jonathan Mukiibi | Blessing Sibanda | Bonaventure F. P. Dossou | Andiswa Bukula | Rooweither Mabuya | Allahsera Auguste Tapo | Edwin Munkoh-Buabeng | Victoire Memdjokam Koagne | Fatoumata Ouoba Kabore | Amelia Taylor | Godson Kalipe | Tebogo Macucwa | Vukosi Marivate | Tajuddeen Gwadabe | Mboning Tchiaze Elvis | Ikechukwu Onyenwe | Gratien Atindogbe | Tolulope Adelani | Idris Akinade | Olanrewaju Samuel | Marien Nahimana | Théogène Musabeyezu | Emile Niyomutabazi | Ester Chimhenga | Kudzai Gotosa | Patrick Mizha | Apelete Agbolo | Seydou Traore | Chinedu Uchechukwu | Aliyu Yusuf | Muhammad Abdullahi | Dietrich Klakow
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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 conditional random field and several multilingual pre-trained language models. We applied various cross-lingual transfer models trained with data available in the UD. Evaluating on the AfricaPOS dataset, we show that choosing the best transfer language(s) in both single-source and multi-source setups greatly improves the POS tagging performance of the target languages, in particular when combined with parameter-fine-tuning methods. Crucially, transferring knowledge from a language that matches the language family and morphosyntactic properties seems to be more effective for POS tagging in unseen languages.

2022

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MasakhaNER 2.0: Africa-centric Transfer Learning for Named Entity Recognition
David Adelani | Graham Neubig | Sebastian Ruder | Shruti Rijhwani | Michael Beukman | Chester Palen-Michel | Constantine Lignos | Jesujoba Alabi | Shamsuddeen Muhammad | Peter Nabende | Cheikh M. Bamba Dione | Andiswa Bukula | Rooweither Mabuya | Bonaventure F. P. Dossou | Blessing Sibanda | Happy Buzaaba | Jonathan Mukiibi | Godson Kalipe | Derguene Mbaye | Amelia Taylor | Fatoumata Kabore | Chris Chinenye Emezue | Anuoluwapo Aremu | Perez Ogayo | Catherine Gitau | Edwin Munkoh-Buabeng | Victoire Memdjokam Koagne | Allahsera Auguste Tapo | Tebogo Macucwa | Vukosi Marivate | Mboning Tchiaze Elvis | Tajuddeen Gwadabe | Tosin Adewumi | Orevaoghene Ahia | Joyce Nakatumba-Nabende
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

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 recently proposed methods like cross-lingual transfer will be effective. In this paper, we aim to move towards solutions for these challenges, focusing on the task of named entity recognition (NER). We present the creation of the largest to-date human-annotated NER dataset for 20 African languages. We study the behaviour of state-of-the-art cross-lingual transfer methods in an Africa-centric setting, empirically demonstrating that the choice of source transfer language significantly affects performance. While much previous work defaults to using English as the source language, our results show that choosing the best transfer language improves zero-shot F1 scores by an average of 14% over 20 languages as compared to using English.

2020

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Proceedings of the first workshop on Resources for African Indigenous Languages
Rooweither Mabuya | Phathutshedzo Ramukhadi | Mmasibidi Setaka | Valencia Wagner | Menno van Zaanen
Proceedings of the first workshop on Resources for African Indigenous Languages

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