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

• URL: http://arxiv.org/abs/2309.10272v2
• Date: Thu, 14 Mar 2024 09:32:16 GMT
• Title: Mixed-Distil-BERT: Code-mixed Language Modeling for Bangla, English, and Hindi
• Authors: Md Nishat Raihan, Dhiman Goswami, Antara Mahmud,
• Abstract summary: 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. Our two-tiered pre-training approach offers efficient alternatives for multilingual and code-mixed language understanding.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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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