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arXiv · 2506.02005

Pruning for Performance: Efficient Idiom and Metaphor Classification in Low-Resource Konkani Using mBERT

Timothy Do·Pranav Saran·Harshita Poojary·Pranav Prabhu·Sean O'Brien·Vasu Sharma·Kevin Zhu
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Abstract

In this paper, we address the persistent challenges that figurative language expressions pose for natural language processing (NLP) systems, particularly in low-resource languages such as Konkani. We present a hybrid model that integrates a pre-trained Multilingual BERT (mBERT) with a bidirectional LSTM and a linear classifier. This architecture is fine-tuned on a newly introduced annotated dataset for metaphor classification, developed as part of this work. To improve the model's efficiency, we implement a gradient-based attention head pruning strategy. For metaphor classification, the pruned model achieves an accuracy of 78%. We also applied our pruning approach to expand on an existing idiom classification task, achieving 83% accuracy. These results demonstrate the effectiveness of attention head pruning for building efficient NLP tools in underrepresented languages.

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Timothy Do, Pranav Saran, Harshita Poojary, Pranav Prabhu, Sean O'Brien, Vasu Sharma, Kevin Zhu. 2025-05-24. Pruning for Performance: Efficient Idiom and Metaphor Classification in Low-Resource Konkani Using mBERT. https://arxiv.org/abs/2506.02005

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