Impact of Recent NLP Advances on Low-Resource Languages and Wikipedia

Ascertain whether recent advances in multilingual natural language processing benefit low-resource languages and determine their specific impact on Wikipedia language editions, particularly for tasks such as source reliability modeling and content quality assessment.

Background

The paper develops language-agnostic features and shows improved performance when training across multiple languages, especially benefiting low-resource settings. At the same time, recent multilingual NLP models have rapidly advanced, potentially changing the landscape for low-resource languages.

The authors explicitly state that it remains unknown whether these developments will tangibly benefit low-resource languages and what specific effects they will have on Wikipedia, motivating future investigation into their applicability for Wikipedia tasks.