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What is 08 Radio+News+Data+Analysis about?

This study focuses on the collection and analysis of a Sepedi-English code-switched radio news corpus to develop a language model for text generation. The research highlights the challenges of low frequency (1.1%) of code-switching in the dataset and the need for larger corpora to improve model training. Results indicate that the model achieved an accuracy of 66% with the collected data, emphasizing the importance of dataset size in enhancing performance.

Author

Matthews Tladi

Language

EN