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Many natural language processing (NLP) tasks make use of massivelypre-trained language models, which are computationally expensive. However,access to high computational resources added to the issue of data scarcity ofAfrican languages constitutes a real barrier to research experiments on theselanguages. In this work, we explore the applicability of low-compute approachessuch as language adapters in the context of this low-resource double-bind. Weintend to answer the following question: do language adapters allow those whoare doubly bound by data and compute to practically build useful models?Through fine-tuning experiments on African languages, we evaluate theireffectiveness as cost-effective approaches to low-resource African NLP. Usingsolely free compute resources, our results show that language adapters achievecomparable performances to massive pre-trained language models which are heavyon computational resources. This opens the door to further experimentation andexploration on full-extent of language adapters capacities.

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