PetKaz at SemEval-2024 Task 3: Advancing Emotion Classification with an LLM for Emotion-Cause Pair Extraction in Conversations
<|reference_start|>PetKaz at SemEval-2024 Task 3: Advancing Emotion Classification with an LLM for Emotion-Cause Pair Extraction in Conversations: 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.<|reference_end|>
@article{kazakov2024petkaz,
    title={PetKaz at SemEval-2024 Task 3: Advancing Emotion Classification with an
  LLM for Emotion-Cause Pair Extraction in Conversations},
    author={Roman Kazakov, Kseniia Petukhova, Ekaterina Kochmar},
    journal={arXiv preprint arXiv:2404.05502},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.05502},
    primaryClass={cs.CL cs.AI}
}