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

In addition, we explore Processing (NLP) have introduced an immense methods to improve the robustness of LMs to improvement in many aspects, including code-mixed text. Using our IndoRobusta-Shot, standardized benchmarks (Wilie et al., 2020; we perform adversarial training to improve the Cahyawijaya et al., 2021; Koto et al., 2020; Winata code-mixed robustness of LMs. We explore three et al., 2022), large pre-trained language model kinds of tuning strategies: 1) code-mix only, 2) (LM) (Wilie et al., 2020; Cahyawijaya et al., 2021; two-steps, and 3) joint training, and empirically Koto et al., 2020), and resource expansion covering search for the best strategy to improve the model local Indonesian languages (Tri Apriani, 2016; robustness on code-mixed data.