Machine Translation for Low-Resource Languages: a community-based participatory approach
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2021
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Swarthmore College. Dept. of Linguistics
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en
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Full copyright to this work is retained by the student author. It may only be used for non-commercial, research, and educational purposes. All other uses are restricted.
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Abstract
Construction of natural language processing products and systems have become increasingly
available and present in research and, for many people, everyday life. However, existing
machine translation systems and research focus on a few highly resourced languages and the
existing methods for their creation are incompatible with many languages’ structure, data
quantity, and language community values. This thesis addresses this gap by using three case
studies to investigate the potential efficacy of a community based participatory model in the
construction of a machine translation system for low-resource languages. It proposes that this
model can be effective if appropriately applied and makes a series of best-practice
recommendations for its application in three areas: algorithm, participants, and data.