IndicTriMix: Developing Language Identification Datasets and Models for Tri-Language Code-Mixing

Abstract:Language identification in code-mixed text, largely observed in social media, is highly essential when users frequently switch between multiple languages within a single utterance. Accurately identifying the languages of code-mixed tokens becomes an urgent necessity. Traditional language identification models, designed for monolingual text, are not well suited for token-level language identification in code-mixed settings. We formulate the task as a sequence labeling problem and fine-tune contextual transformer-based models MuRIL and XLM-RoBERTa best suited for Indian languages. We evaluate these systems on three different data configurations (Hindi, Gujarati, and Bengali) to predict language labels for individual tokens. We release a benchmark for language identification in code-mixed tokens with manually annotated test sets. We propose two approaches of code-mixed generation using parallel sentences of three languages. The trained models demonstrate the effectiveness of contextual embeddings for token-level language identification in multilingual social media text. For reproducibility and to facilitate future research, we publicly release our fine-tuned models.

Computation and Language (cs.CL)

arXiv:2609.11851 [cs.CL]

(or arXiv:2609.11851v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2609.11851 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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