Exploring Machine Translation for code-switching between English and Setswana in South African classrooms
Keywords: code switching, NLP
TL;DR: MT for CS between English and Setswana in SAn classrooms
Abstract: One of the major challenges of the Department of Education in South
Africa is the low numeracy skills amongst South African learners. This
study seeks to spotlight the low numeracy challenge encountered in the
learning and teaching of mathematics in a classroom where the educator
and learners are Setswana native speakers, but use English as the
language of learning and teaching. Using English as a language of learning
and teaching mathematics to non-native English speaking learners
has been stated as one of the reasons why learners perform poorly in
mathematics, leading to low numeracy skills. It has been shown that
when educators code-switch between English and the native language
of the learners to explain mathematical concepts, learners tend to participate
more in the classroom and perform better in mathematics. Codeswitching
is a topic of interest in Natural Language Processing. Pretrained
language models (PLM), such as the mT5, have previously been
used in machine translation of code-switched data. In this research, a
small corpus of parallel text consisting of monolingual English mathematical
text translated into English-Setswana (code-mixed English and
Setswana) will be used to fine-tune the mT5 PLM for the purpose of
translating mathematical text from English to English/Setswana. In addition,
the M2M-100 PLM has been leveraged by African researchers for
the machine translation of low-resourced languages. This research aims
to fine-tune the M2M-100 PLM using the same corpus and to evaluate
its performance on the task of machine translation to potentially aid in
the learning and teaching of mathematics in South Africa.
Submission Category: Machine learning algorithms
Submission Number: 48
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