Abstract
Code-switching is a phenomenon that occurs mostly in multilingual countries where multilingual speakers often switch between languages in their conversations. The unavailability of large-scale code-switched corpora hampers the development and training of language models for the generation of code-switched text. In this study, we explore the initial phase of collecting and creating Sepedi-English code-switched corpus for generating synthetic news. Radio news and the frequency of code-switching on read news were considered and analysed. We developed and trained a Transformer-based language model using the collected code-switched dataset. We observed that the frequency of code-switched data in the dataset was very low at 1.1 %. We complemented our dataset with the news headlines dataset to create a new dataset. Although the frequency was still low, the model obtained the optimal loss rate of 2,361 with an accuracy of 66 %.
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