MasakhaPOS: Part-of-Speech Tagging for Typologically Diverse African Languages
Loading...
Date
2023-05-23
Journal Title
Journal ISSN
Volume Title
Publisher
arXiv preprint arXiv
Abstract
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 pretrained
language models. We applied various
cross-lingual transfer models trained with data
available in UD. Evaluating on the Masakha-
POS 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.
Description
Keywords
Citation
Dione, C. M. B., Adelani, D., Nabende, P., Alabi, J., Sindane, T., Buzaaba, H., ... & Klakow, D. (2023). Masakhapos: Part-of-speech tagging for typologically diverse african languages. arXiv preprint arXiv:2305.13989.