Many natural language processing (NLP) tasks make use of massively
pre-trained language models, which are computationally expensive. However,
access to high computational resources added to the issue of data scarcity of
African languages constitutes a real barrier to research experiments on these
languages. In this work, we explore the applicability of low-compute approaches
such as language adapters in the context of this low-resource double-bind. We
intend to answer the following question: do language adapters allow those who
are doubly bound by data and compute to practically build useful models?
Through fine-tuning experiments on African languages, we evaluate their
effectiveness as cost-effective approaches to low-resource African NLP. Using
solely free compute resources, our results show that language adapters achieve
comparable performances to massive pre-trained language models which are heavy
on computational resources. This opens the door to further experimentation and
exploration on full-extent of language adapters capacities.

通过对非洲语言 NLP 的低成本实验，我们评估了语言适配器的效果，并发现它们可以在仅使用免费计算资源的情况下与计算资源密集型的大规模预训练语言模型相媲美，从而打开了进一步实验和探索的可能。

适应低资源双重限制：研究在非洲低资源语言中使用低计算方法

Adapting to the Low-Resource Double-Bind: Investigating Low-Compute Methods on Low-Resource African Languages

We take a step towards addressing the under-representation of the African
continent in NLP research by creating the first large publicly available
high-quality dataset for named entity recognition (NER) in ten African
languages, bringing together a variety of stakeholders. We detail
characteristics of the languages to help researchers understand the challenges
that these languages pose for NER. We analyze our datasets and conduct an
extensive empirical evaluation of state-of-the-art methods across both
supervised and transfer learning settings. We release the data, code, and
models in order to inspire future research on African NLP.

本文介绍了第一个大型公开高质量的非洲十种语言的命名实体识别数据集，研究这些语言对命名实体识别带来的挑战，并在监督和迁移学习设置下进行了广泛的实证评估和分析，旨在启发未来的非洲自然语言处理研究。