Participatory Research for Low-resourced Machine Translation:A Case Study in African Languages

Research in NLP lacks geographic diversity, and the question of how NLP can be scaled to low-resourced languages has not yet been adequately solved. "Low-resourced"-ness is a complex problem going beyond data availability and reflects systemic problems in society. In this paper, we focus on the task of Machine Translation (MT), that plays a crucial role for information accessibility and communication worldwide. Despite immense improvements in MT over the past decade, MT is centered around a few high-resourced languages. As MT researchers cannot solve the problem of low-resourcedness alone, we propose participatory research as a means to involve all necessary agents required in the MT development process. We demonstrate the feasibility and scalability of participatory research with a case study on MT for African languages. Its implementation 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. Benchmarks, models, data, code, and evaluation results are released under https://github.com/masakhane-io/masakhane-mt

Online Learning Experiences of Canadian Black Nova Scotians during Covid-19: Adopting an Intersectionality Framework

Though school closures due to the COVID-19 pandemic affected all students globally, the effect was significantly more for students from marginalized and vulnerable communities. In Nova Scotia, Canada, the concern was the racial achievement gap that the education system is addressing through an inclusive education policy. The worry, especially for Black Nova Scotian students, was the online learning demands and the associated challenges. Through an analysis of a household survey and intersectionality framework, we explored these challenges. We argue that students have multiple and simultaneously acting identities that lead to differential learning experiences and outcomes, and an intersectionality approach should be considered to inform education improvement decisions. Keywords: online learning, Black Canadians, intersectionality, household survey, structural equation modelling Bien que les fermetures d'écoles dues à la pandémie de COVID-19 aient touché tous les élèves du monde, l'effet a été nettement plus marqué pour les élèves issus de communautés marginalisées et vulnérables. En Nouvelle-Écosse, au Canada, l'inquiétude portait sur l'écart de réussite raciale que le système d’éducation s'efforce de combler par une politique d'éducation inclusive. L'inquiétude, en particulier pour les élèves noirs de Nouvelle-Écosse, portait sur les exigences de l'apprentissage en ligne et les défis qui y sont associés. Par l'analyse d'une enquête auprès des ménages et d'un cadre d'intersectionnalité, nous avons exploré ces défis. Nous soutenons que les élèves ont des identités multiples qui agissent simultanément et mènent à des expériences et des résultats d'apprentissage différents, et qu'une approche d'intersectionnalité devrait être considérée pour informer les décisions portant sur l’amélioration de l'éducation. Mots clés : apprentissage en ligne, Canadiens noirs, intersectionnalité, enquête auprès des ménages, modélisation par équations structurelles