Cheikh M. Bamba Dione, David Ifeoluwa Adelani, Peter Nabende, Jesujoba Oluwadara Alabi, Thapelo Sindane, Happy Buzaaba, Shamsuddeen Hassan Muhammad, Chris C. Emezue, Perez Ogayo, Anuoluwapo Aremu, Catherine Gitau, Derguene Mbaye, Jonathan Mukiibi, Blessing K. Sibanda, Bonaventure F. P. Dossou, Andiswa Bukula, Rooweither Mabuya, A. Tapo, Edwin Munkoh-Buabeng, V. M. Koagne, F. Kabore, Amelia Taylor, Godson Kalipe, Tebogo Macucwa, Vukosi Marivate, T. Gwadabe, Mboning Tchiaze Elvis, I. Onyenwe, G. Atindogbé, T. Adelani, Idris Akinade, Olanrewaju Samuel, M. Nahimana, Th’eogene Musabeyezu, Emile Niyomutabazi, Ester Chimhenga, Kudzai Gotosa, Patrick Mizha, Apelete Agbolo, Seydou T. Traoré, C. Uchechukwu, Aliyu Yusuf, M. Abdullahi, D. Klakow

ACL

Paper

Abstract

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.