arXiv:2404.05502
공식 코드 · Papers with Code
PetKaz at SemEval-2024 Task 3: Advancing Emotion Classification with an LLM for Emotion-Cause Pair Extraction in Conversations
원문 초록
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.
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AIMA at SemEval-2024 Task 3: Simple Yet Powerful Emotion Cause Pair Analysis
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MMAFFBen: A Multilingual and Multimodal Affective Analysis Benchmark for Evaluating LLMs and VLMs
What About Emotions? Guiding Fine-Grained Emotion Extraction from Mobile App Reviews
EmoMeta: A Multimodal Dataset for Fine-grained Emotion Classification in Chinese Metaphors
MELLM: Exploring LLM-Powered Micro-Expression Understanding Enhanced by Subtle Motion Perception
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