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

• URL: http://arxiv.org/abs/2303.16985v1
• Date: Wed, 29 Mar 2023 19:25:43 GMT
• Title: Adapting to the Low-Resource Double-Bind: Investigating Low-Compute Methods on Low-Resource African Languages
• Authors: Colin Leong, Herumb Shandilya, Bonaventure F. P. Dossou, Atnafu Lambebo Tonja, Joel Mathew, Abdul-Hakeem Omotayo, Oreen Yousuf, Zainab Akinjobi, Chris Chinenye Emezue, Shamsudeen Muhammad, Steven Kolawole, Younwoo Choi, Tosin Adewumi
• Abstract summary: Access to high computational resources added to the issue of data scarcity of African languages. We evaluate language adapters as cost-effective approaches to low-resource African NLP. This opens the door to further experimentation and exploration on full-extent of language adapters capacities.
• Score: 0.6833698896122186
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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