تقرير
Adapting to the Low-Resource Double-Bind: Investigating Low-Compute Methods on Low-Resource African Languages
| العنوان: | Adapting to the Low-Resource Double-Bind: Investigating Low-Compute Methods on Low-Resource African Languages |
|---|---|
| المؤلفون: | Leong, Colin, Shandilya, Herumb, Dossou, Bonaventure F. P., Tonja, Atnafu Lambebo, Mathew, Joel, Omotayo, Abdul-Hakeem, Yousuf, Oreen, Akinjobi, Zainab, Emezue, Chris Chinenye, Muhammad, Shamsudeen, Kolawole, Steven, Choi, Younwoo, Adewumi, Tosin |
| سنة النشر: | 2023 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Computation and Language, Computer Science - Artificial Intelligence |
| الوصف: | 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. Comment: Accepted to AfricaNLP workshop at ICLR2023 |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2303.16985 |
| رقم الأكسشن: | edsarx.2303.16985 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |