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。

MasakhaNER 2.0：面向非洲的命名实体识别迁移学习

MasakhaNER 2.0: Africa-centric Transfer Learning for Named Entity Recognition

Most combinations of NLP tasks and language varieties lack in-domain examples
for supervised training because of the paucity of annotated data. How can
neural models make sample-efficient generalizations from task-language
combinations with available data to low-resource ones? In this work, we propose
a Bayesian generative model for the space of neural parameters. We assume that
this space can be factorized into latent variables for each language and each
task. We infer the posteriors over such latent variables based on data from
seen task-language combinations through variational inference. This enables
zero-shot classification on unseen combinations at prediction time. For
instance, given training data for named entity recognition (NER) in Vietnamese
and for part-of-speech (POS) tagging in Wolof, our model can perform accurate
predictions for NER in Wolof. In particular, we experiment with a typologically
diverse sample of 33 languages from 4 continents and 11 families, and show that
our model yields comparable or better results than state-of-the-art, zero-shot
cross-lingual transfer methods. Moreover, we demonstrate that approximate
Bayesian model averaging results in smoother predictive distributions, whose
entropy inversely correlates with accuracy. Hence, the proposed framework also
offers robust estimates of prediction uncertainty. Our code is located at
github.com/cambridgeltl/parameter-factorization

本文提出了一种贝叶斯生成模型，用于在任务 - 语言组合间进行零样本分类，实现从有限数据向低资源语种的泛化，并已在 33 种语言上进行了实验验证。