IndoRobusta: Towards Robustness Against Diverse Code-Mixed Indonesian
  Local Languages
Muhammad Farid Adilazuarda Samuel Cahyawijaya Genta Indra Winata Pascale Fung Ayu Purwarianti
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.