African languages are spoken by over a billion people, but are
underrepresented in NLP research and development. The challenges impeding
progress include the limited availability of annotated datasets, as well as a
lack of understanding of the settings where current methods are effective. In
this paper, we make progress towards solutions for these challenges, focusing
on the task of named entity recognition (NER). We create the largest
human-annotated NER dataset for 20 African languages, and we study the behavior
of state-of-the-art cross-lingual transfer methods in an Africa-centric
setting, demonstrating that the choice of source language significantly affects
performance. We show that choosing the best transfer language improves
zero-shot F1 scores by an average of 14 points across 20 languages compared to
using English. Our results highlight the need for benchmark datasets and models
that cover typologically-diverse African languages.

本文研究非洲语言在自然语言处理上的挑战，针对命名实体识别任务，创造了包含 20 种非洲语言的最大人工标注数据集，研究了现有跨语言传递方法的表现，并发现选择最佳传递语言相较使用英语，可以提高零 - shot F1 分数 14 点。