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Workshop: The 4th Workshop on practical ML for Developing Countries: learning under limited/low resource settings

Combating Harmful Hype in Natural Language Processing

Asmelash Hadgu · Paul Azunre · Timnit Gebru


In recent years, large multinational corporations have made claims of creating “general purpose” models that can handle many different tasks within natural language processing. Recent works from Meta for example, give theimpression that they have nearly solved machine translation tasks for more than 200 languages including 55 African languages. In this paper, we outline the harms speakers of non dominant languages have experienced due to these grandiose and inaccurate claims, ranging from diverting resources from local startups serving specific communities, to low quality datasets and models from these corporations. We urge the African NLP and machine learning communities to push back against these claims, and support smaller organizations serving their own communities.

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