MasakhaPOS: Part-of-Speech Tagging for Typologically Diverse African
  Languages
Cheikh M. Bamba Dione David 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
In this paper, we present MasakhaPOS, 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 UD (universal
dependencies) guidelines. We conducted extensive POS baseline experiments using
conditional random field and several multilingual pre-trained language models.
We applied various cross-lingual transfer models trained with data available in
UD. Evaluating on the MasakhaPOS 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 cross-lingual parameter-efficient fine-tuning methods.
Crucially, transferring knowledge from a language that matches the language
family and morphosyntactic properties seems more effective for POS tagging in
unseen languages.