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A Journey Through the Opportunity of Low Resourced Natural Language Processing — An African Lens
Vukosi Marivate · David Adelani

Mon Dec 06 05:00 AM -- 08:00 AM (PST) @ Virtual

Low Resourced languages pose an interesting challenge for Machine Learning algorithms, representation, data collection and accessibility of machine learning in general. In this tutorial, we work to provide a journey through machine learning in low resourced languages that covers a breadth of sub topics and depth in some of the areas of focus. We will do this through the lens of Natural Language processing for African languages. We present some historical context, recent advances and current opportunities that researchers can take advantage of to do impactful research in this area. We hope for this tutorial to not only shed light on the subject area, but to expand the number of practitioners who interact in a thoughtful and considerate way with the wider ML community working in these areas. We hope this to be as interactive as possible and to provide resources for researchers to tackle the challenges.

Author Information

Vukosi Marivate (University of Pretoria)

Vukosi Marivate (https://www.vima.co.za/) holds a PhD in Computer Science (Rutgers University, as Fulbright Science and Technology Fellow) and MSc & BSc in Electrical Engineering (Wits University). Dr Marivate is based at the University of Pretoria as the UP ABSA Chair of Data Science. He works on developing Machine Learning/Artificial Intelligence methods to extract insights from data. A large part of his work over the last few years has been in the intersection of Machine Learning and Natural Language Processing (NLP). This has led to research outputs focused on how we can better improve low resource language tools, especially for African Languages. This has included creating new software libraries, new research approaches for robust NLP and encouraging the development of datasets for African languages. As part of his vision for the ABSA Data Science chair, Vukosi is interested in Data Science for Social Impact (https://dsfsi.github.io/), using local challenges as a springboard for research. In this area, Vukosi has worked on projects in science, energy, public safety and utilities. Vukosi is cofounder of the Deep Learning Indaba, the largest Machine Learning/Artificial Intelligence workshop on the African continent, aiming to strengthen African Machine Learning

David Adelani (MPI-SWS)

David Adelani is a doctoral student in computer science at Saarland University, Saarbrücken, Germany. His current research focuses on the security and privacy of users’ information in dialogue systems and online social interactions. Originally from Nigeria, he is also actively involved in the development of natural language processing datasets and tools for low-resource languages, with a special focus on African languages.

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