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. "Lowresourced"-ness is a complex problem going beyond data availability and reflects systemic problems in society. * ∀ to represent the whole Masakhane community.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 at https://github. com/masakhane-io/masakhane-mt.
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. "Lowresourced"-ness is a complex problem going beyond data availability and reflects systemic problems in society. * ∀ to represent the whole Masakhane community.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 at https://github. com/masakhane-io/masakhane-mt.
As urban areas around the world seek to transform themselves into smart cities, new technologies are being interwoven into the urban fabric that surrounds us. This process is often technology-driven and revolves around issues of quantification, cost reduction, and efficiency. This perspective is increasingly being challenged by more inclusive perspectives that seek to empower civil society and which focus on its needs and demands. A major concern with smart city technologies in public spaces is the lack of protection for individual privacy as a result of surveillance. In this paper, we have chosen the use case of traffic counting as an example to illustrate how the use of advanced technologies and integrated planning strategies can shift the balance between administrative and business interests on the one hand, and privacy concerns on the other, towards a privacy-centric approach. We propose a privacy-centric planning and development approach for smart city technologies. Through our use case, we demonstrate a privacy-centric participatory development process that led to a prototypical technical solution for privacy-friendly and human-centric traffic counting. We conclude this paper by deriving suggestions for more privacyfriendly smart city development processes from our specific use case.
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