Bonaventure F. P. Dossou

TL;DR: The feasibility and scalability of participatory research is demonstrated with a case study on MT for African languages, which leads to a collection of novel translation datasets, MT benchmarks for over 30 languages, with human evaluations for a third of them, and enables participants without formal training to make a unique scientific contribution.

TL;DR: This work proposes an active learning algorithm leveraging epistemic uncertainty estimation and the recently proposed GFlowNets as a generator of diverse candidate solutions, with the objective to obtain a diverse batch of useful and novel batches with high scoring candidates after each round.

TL;DR: The methodology for building the community and spurring research from the African continent, as well as the success of the community in terms of addressing the identified problems affecting African NLP are discussed.

TL;DR: It is demonstrated that the most effective strategy for transferring both additional languages and additional domains is to leverage small quantities of high-quality translation data to fine-tune large pre-trained models.

TL;DR: In this paper, the authors manually audit the quality of 205 language-specific corpora released with five major public datasets (CCAligned, ParaCrawl, WikiMatrix, OSCAR, mC4) and audit the correctness of language codes in a sixth (JW300).