PetKaz at SemEval-2024 Task 3: Advancing Emotion Classification with an LLM for Emotion-Cause Pair Extraction in Conversations
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Abstract (arXiv)
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.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 6 of its 15 references that are in the map. First posted on arXiv 8 Apr 2024.
Builds on
6 of 15 references.
References · 6
- 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
- 2019Emotion-Cause Pair Extraction: A New Task to Emotion Analysis in Textscited by 82
- 2020Recognizing Emotion Cause in Conversationscited by 66
- 2021Multimodal Emotion-Cause Pair Extraction in Conversationscited by 30
- 2023How to Enhance Causal Discrimination of Utterances: A Case on Affective Reasoningcited by 3
- 2022Discourse-Aware Emotion Cause Extraction in Conversationscited by 1
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Citing papers per year: 2024: 1; 2025: 1.
2 papers.