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This work proposes two approaches of code-mixed generation using parallel sentences of three languages and fine-tune contextual transformer-based models MuRIL and XLM-RoBERTa best suited for Indian languages for language identification in code-mixed settings.
Machine translation benchmarks have functionally saturated, but pairing human-authored failure cases with deterministic verification rules reveals critical multimodal blind spots that automated metrics consistently miss.
