Knowledge Distillation from Monolingual to Multilingual Models for Intelligent and Interpretable Multilingual Emotion Detection

Emotion detection from text is a crucial task in understanding natural language with wide-ranging applications. Existing approaches for multilingual emotion detection from text face challenges with data scarcity across many languages and a lack of interpretability. We propose a novel method that leverages both monolingual and multilingual pre-trained language models to improve performance and interpretability. Our approach involves 1) training a high-performing English monolingual model in parallel with a multilingual model and 2) using knowledge distillation to transfer the emotion detection capabilities from the monolingual teacher to the multilingual student model. Experiments on a multilingual dataset demonstrate significant performance gains for refined multilingual models like XLM-RoBERTa and E5 after distillation. Furthermore, our approach enhances interpretability by enabling better identification of emotion-trigger words. Our work presents a promising direction for building accurate, robust and explainable multilingual emotion detection systems.