CMLFormer: A Dual Decoder Transformer with Switching Point Learning for Code-Mixed Language Modeling

Aditeya Baral; Allen George Ajith; Roshan Nayak; Mrityunjay Abhijeet Bhanja

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arXiv · 2505.12587

CMLFormer: A Dual Decoder Transformer with Switching Point Learning for Code-Mixed Language Modeling

Aditeya Baral·Allen George Ajith·Roshan Nayak·Mrityunjay Abhijeet Bhanja

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Abstract

Code-mixed languages, characterized by frequent within-sentence language transitions, present structural challenges that standard language models fail to address. In this work, we propose CMLFormer, an enhanced multi-layer dual-decoder Transformer with a shared encoder and synchronized decoder cross-attention, designed to model the linguistic and semantic dynamics of code-mixed text. CMLFormer is pre-trained on an augmented Hinglish corpus with switching point and translation annotations with multiple new objectives specifically aimed at capturing switching behavior, cross-lingual structure, and code-mixing complexity. Our experiments show that CMLFormer improves F1 score, precision, and accuracy over other approaches on the HASOC-2021 benchmark under select pre-training setups. Attention analyses further show that it can identify and attend to switching points, validating its sensitivity to code-mixed structure. These results demonstrate the effectiveness of CMLFormer's architecture and multi-task pre-training strategy for modeling code-mixed languages.

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Aditeya Baral, Allen George Ajith, Roshan Nayak, Mrityunjay Abhijeet Bhanja. 2025-05-19. CMLFormer: A Dual Decoder Transformer with Switching Point Learning for Code-Mixed Language Modeling. https://arxiv.org/abs/2505.12587

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