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

• URL: http://arxiv.org/abs/2311.12405v1
• Date: Tue, 21 Nov 2023 07:50:53 GMT
• Title: IndoRobusta: Towards Robustness Against Diverse Code-Mixed Indonesian Local Languages
• Authors: Muhammad Farid Adilazuarda, Samuel Cahyawijaya, Genta Indra Winata, Pascale Fung, Ayu Purwarianti
• Abstract summary: We explore code-mixing in Indonesian with four embedded languages, i.e., English, Sundanese, Javanese, and Malay. Our analysis shows that the pre-training corpus bias affects the model's ability to better handle Indonesian-English code-mixing.
• Score: 62.60787450345489
• License: http://creativecommons.org/licenses/by-sa/4.0/
• 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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