Mixed-Distil-BERT: Code-mixed Language Modeling for Bangla, English, and Hindi

Md Nishat Raihan; Dhiman Goswami; Antara Mahmud

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arXiv · 2309.10272

Mixed-Distil-BERT: Code-mixed Language Modeling for Bangla, English, and Hindi

Md Nishat Raihan·Dhiman Goswami·Antara Mahmud

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Abstract

One of the most popular downstream tasks in the field of Natural Language Processing is text classification. Text classification tasks have become more daunting when the texts are code-mixed. Though they are not exposed to such text during pre-training, different BERT models have demonstrated success in tackling Code-Mixed NLP challenges. Again, in order to enhance their performance, Code-Mixed NLP models have depended on combining synthetic data with real-world data. It is crucial to understand how the BERT models' performance is impacted when they are pretrained using corresponding code-mixed languages. In this paper, we introduce Tri-Distil-BERT, a multilingual model pre-trained on Bangla, English, and Hindi, and Mixed-Distil-BERT, a model fine-tuned on code-mixed data. Both models are evaluated across multiple NLP tasks and demonstrate competitive performance against larger models like mBERT and XLM-R. Our two-tiered pre-training approach offers efficient alternatives for multilingual and code-mixed language understanding, contributing to advancements in the field.

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BibTeX RIS

Md Nishat Raihan, Dhiman Goswami, Antara Mahmud. 2024-03-14. Mixed-Distil-BERT: Code-mixed Language Modeling for Bangla, English, and Hindi. https://arxiv.org/abs/2309.10272

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