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arXiv · 2311.12405

IndoRobusta: Towards Robustness Against Diverse Code-Mixed Indonesian Local Languages

Muhammad Farid Adilazuarda·Samuel Cahyawijaya·Genta Indra Winata·Pascale Fung·Ayu Purwarianti
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Abstract

Significant progress has been made on Indonesian NLP. Nevertheless, exploration of the code-mixing phenomenon in Indonesian is limited, despite many languages being frequently mixed with Indonesian in daily conversation. In this work, we explore code-mixing in Indonesian with four embedded languages, i.e., English, Sundanese, Javanese, and Malay; and introduce IndoRobusta, a framework to evaluate and improve the code-mixing robustness. Our analysis shows that the pre-training corpus bias affects the model's ability to better handle Indonesian-English code-mixing when compared to other local languages, despite having higher language diversity.

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Muhammad Farid Adilazuarda, Samuel Cahyawijaya, Genta Indra Winata, Pascale Fung, Ayu Purwarianti. 2023-11-21. IndoRobusta: Towards Robustness Against Diverse Code-Mixed Indonesian Local Languages. https://arxiv.org/abs/2311.12405

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