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.

本文提出了 MasakhaPOS 数据集，并利用条件随机场、多语言预训练语言模型、跨语言传递模型以及交叉语言参数高效微调等方法来解决非常规非洲语言的词性标注问题。实验结果表明，选择最佳的传递语言可显著提高目标语言的 POS 标注性能，特别是结合跨语言参数高效微调方法时。关键是，从与目标语言匹配的语言传递知识似乎对未见过的语言的 POS 标记更有效。