PetKaz at SemEval-2024 Task 3: Advancing Emotion Classification with an LLM for Emotion-Cause Pair Extraction in Conversations
This work addresses emotion analysis in conversational AI, but it is incremental as it builds on existing methods for a specific competition task.
The paper tackled emotion-cause pair extraction in conversations by combining fine-tuned GPT-3.5 for emotion classification with a BiLSTM-based neural network for cause detection, achieving a weighted-average proportional F1 score of 0.264 and ranking 2nd in Subtask 1.
In this paper, we present our submission to the SemEval-2023 Task~3 "The Competition of Multimodal Emotion Cause Analysis in Conversations", focusing on extracting emotion-cause pairs from dialogs. Specifically, our approach relies on combining fine-tuned GPT-3.5 for emotion classification and a BiLSTM-based neural network to detect causes. We score 2nd in the ranking for Subtask 1, demonstrating the effectiveness of our approach through one of the highest weighted-average proportional F1 scores recorded at 0.264.