Using ML to close the vocabulary gap in the context of environment and climate change in Chichewa (Proposals Track)
Amelia Taylor (University of Malawi, The Polytechnic)
Abstract
In the west, alienation from nature and deteriorating opportunities to experience it, have led educators to incorporate educational programs in schools, to bring pupils in contact with nature and to enhance their understanding of issues related to the environment and its protection. In Africa, and in Malawi, where most people engage in agriculture, and spend most of their time in the 'outdoors', alienation from nature is happening too, although in different ways. Large portion of the indigenous vocabulary and knowledge remains unknown or is slowly disappearing, and there is a need to build a glossary of terms regarding environment and climate change in the vernacular to improve the dialog regarding climate change and environmental protection.. We believe that ML has a role to play in closing the ‘vocabulary gap’ of terms and concepts regarding the environment and climate change that exists in Chichewa and other Malawian languages by helping to creating a visual dictionary of key terms used to describe the environment and explain the issues involved in climate change and their meaning. Chichewa is a descriptive language, one English term may be translated using several words. Thus, the task is not to detect just literal translations, but also translations by means of ‘descriptions’ and illustrations and thus extract correspondence between terms and definitions and to measure how appropriate a term is to convey the meaning intended. As part of this project, ML can be used to identify ‘loanword patterns’, which may be useful in understanding the transmission of cultural items.