mlsanityData to 14 Oct 2026

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

arXiv 2311.12405 ↗First posted 21 Nov 2023cs.CLcited by 5

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Abstract (arXiv)

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.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 31 of its 54 references that are in the map. First posted on arXiv 21 Nov 2023.

Builds on

31 of 54 references.

References · 31

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19 more
  1. 2021IndoNLG: Benchmark and Resources for Evaluating Indonesian Natural Language Generationcited by 57
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  19. 2019Code-switching Language Modeling With Bilingual Word Embeddings: A Case Study for Egyptian Arabic-Englishcited by 6

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 2; 2025: 2; 2026: 1.

5 papers.