Prompting towards Alleviating Code-Switched Data Scarcity in Under-Resourced Languages with GPT as a Pivot

Michelle Terblanche, Kayode Olaleye, Vukosi Marivate

Abstract
Many multilingual communities, including numerous in Africa, frequently engage in code-switching during conversations. This behaviour stresses the need for natural language processing technologies adept at processing code-switched text. However, data scarcity, particularly in African languages, poses a significant challenge, as many are low-resourced and under-represented. In this study, we prompted GPT 3.5 to generate Afrikaans–English and Yoruba–English code-switched sentences, enhancing diversity using topic-keyword pairs, linguistic guidelines, and few-shot examples. Our findings indicate that the quality of generated sentences for languages using non-Latin scripts, like Yoruba, is considerably lower when compared with the high Afrikaans–English success rate. There is therefore a notable opportunity to refine prompting guidelines to yield sentences suitable for the fine-tuning of language models. We propose a framework for augmenting the diversity of synthetically generated code-switched data using GPT and propose leveraging this technology to mitigate data scarcity in low-resourced languages, underscoring the essential role of native speakers in this process.
Anthology ID:
2024.sigul-1.33
Volume:
Proceedings of the 3rd Annual Meeting of the Special Interest Group on Under-resourced Languages @ LREC-COLING 2024
Month:
May
Year:
2024
Address:
Torino, Italia
Editors:
Maite Melero, Sakriani Sakti, Claudia Soria
Venues:
SIGUL | WS
SIG:
Publisher:
ELRA and ICCL
Note:
Pages:
272–282
Language:
URL:
https://preview.aclanthology.org/add_missing_videos/2024.sigul-1.33/
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Cite (ACL):
Michelle Terblanche, Kayode Olaleye, and Vukosi Marivate. 2024. Prompting towards Alleviating Code-Switched Data Scarcity in Under-Resourced Languages with GPT as a Pivot. In Proceedings of the 3rd Annual Meeting of the Special Interest Group on Under-resourced Languages @ LREC-COLING 2024, pages 272–282, Torino, Italia. ELRA and ICCL.
Cite (Informal):
Prompting towards Alleviating Code-Switched Data Scarcity in Under-Resourced Languages with GPT as a Pivot (Terblanche et al., SIGUL 2024)
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https://preview.aclanthology.org/add_missing_videos/2024.sigul-1.33.pdf
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