Metraipolis › archive › dc_04048fe8fe8a

IndicTriMix: Developing Language Identification Datasets and Models for Tri-Language Code-Mixing — arXiv:2609.11851

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.11851
License (as archived): CC0-1.0
Archived: 2026-10-16T05:44:18.887Z
SHA-256 of the archived text: 414acf95efb10aa55aa5856646e6750e60b27a54aac7b866518ca2487c5c28b1
Stable URL of this page: https://metraipolis.com/documento/dc_04048fe8fe8a

How to cite

IndicTriMix: Developing Language Identification Datasets and Models for Tri-Language Code-Mixing — arXiv:2609.11851 — arXiv (en), https://arxiv.org/abs/2609.11851 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_04048fe8fe8a (Metraipolis archive dc_04048fe8fe8a, sha256 414acf95efb10aa55aa5856646e6750e60b27a54aac7b866518ca2487c5c28b1)

BibTeX

@misc{dc04048fe8fe8a,
  title = {IndicTriMix: Developing Language Identification Datasets and Models for Tri-Language Code-Mixing — arXiv:2609.11851},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.11851},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_04048fe8fe8a; sha256 414acf95efb10aa55aa5856646e6750e60b27a54aac7b866518ca2487c5c28b1},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.11851. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

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.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-09-11 in cs.CL).
Categorie arXiv: Computation and Language (cs.CL).
Pagina del lavoro: https://arxiv.org/abs/2609.11851 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-16T05:23:57.025Z dalla pagina https://arxiv.org/catchup/cs.CL/2026-09-11?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).