Fatoumata Ouoba Kabore


2023

pdf
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

pdf
A Few Thousand Translations Go a Long Way! Leveraging Pre-trained Models for African News Translation
David Adelani | Jesujoba Alabi | Angela Fan | Julia Kreutzer | Xiaoyu Shen | Machel Reid | Dana Ruiter | Dietrich Klakow | Peter Nabende | Ernie Chang | Tajuddeen Gwadabe | Freshia Sackey | Bonaventure F. P. Dossou | Chris Emezue | Colin Leong | Michael Beukman | Shamsuddeen Muhammad | Guyo Jarso | Oreen Yousuf | Andre Niyongabo Rubungo | Gilles Hacheme | Eric Peter Wairagala | Muhammad Umair Nasir | Benjamin Ajibade | Tunde Ajayi | Yvonne Gitau | Jade Abbott | Mohamed Ahmed | Millicent Ochieng | Anuoluwapo Aremu | Perez Ogayo | Jonathan Mukiibi | Fatoumata Ouoba Kabore | Godson Kalipe | Derguene Mbaye | Allahsera Auguste Tapo | Victoire Memdjokam Koagne | Edwin Munkoh-Buabeng | Valencia Wagner | Idris Abdulmumin | Ayodele Awokoya | Happy Buzaaba | Blessing Sibanda | Andiswa Bukula | Sam Manthalu
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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 excluded from the large-scale crawls for datasets. Furthermore, downstream users of these models are restricted to the selection of languages originally chosen for pre-training. This work investigates how to optimally leverage existing pre-trained models to create low-resource translation systems for 16 African languages. We focus on two questions: 1) How can pre-trained models be used for languages not included in the initial pretraining? and 2) How can the resulting translation models effectively transfer to new domains? To answer these questions, we create a novel African news corpus covering 16 languages, of which eight languages are not part of any existing evaluation dataset. We demonstrate that the most effective strategy for transferring both additional languages and additional domains is to leverage small quantities of high-quality translation data to fine-tune large pre-trained models.