Pruning for Performance: Efficient Idiom and Metaphor Classification in Low-Resource Konkani Using mBERT
This work addresses the problem of building efficient NLP tools for underrepresented languages, though it is incremental as it applies existing pruning techniques to a new domain.
The paper tackled the challenge of figurative language classification in low-resource Konkani by developing a hybrid mBERT-LSTM model with gradient-based attention head pruning, achieving 78% accuracy for metaphor classification and 83% for idiom classification.
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.