  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.
