Lotus at SemEval-2025 Task 11: RoBERTa with Llama-3 Generated Explanations for Multi-Label Emotion Classification
This addresses emotion detection tasks by improving classification accuracy, though it appears incremental as it builds on existing models like RoBERTa and Llama-3.
The paper tackled multi-label emotion classification by using Llama-3 to generate explanatory content to clarify ambiguous emotional expressions, enhancing RoBERTa's performance and improving F1-scores for emotions like fear, joy, and sadness.
This paper presents a novel approach for multi-label emotion detection, where Llama-3 is used to generate explanatory content that clarifies ambiguous emotional expressions, thereby enhancing RoBERTa's emotion classification performance. By incorporating explanatory context, our method improves F1-scores, particularly for emotions like fear, joy, and sadness, and outperforms text-only models. The addition of explanatory content helps resolve ambiguity, addresses challenges like overlapping emotional cues, and enhances multi-label classification, marking a significant advancement in emotion detection tasks.