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

2026-09-10

AI Summary

研究针对社交媒体中的三语代码混用现象，通过序列标注问题和微调基于上下文的转换模型来准确识别代码混用文本中的语言。

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.

Problem

• language identification
• code-mixed text
• social media
• multilingual

Innovation

• code-mixed text
• sequence labeling
• contextual embeddings
• multilingual social media