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.
@article{arxiv.2506.02005,
title = {Pruning for Performance: Efficient Idiom and Metaphor Classification in Low-Resource Konkani Using mBERT},
author = {Timothy Do and Pranav Saran and Harshita Poojary and Pranav Prabhu and Sean O'Brien and Vasu Sharma and Kevin Zhu},
journal= {arXiv preprint arXiv:2506.02005},
year = {2025}
}