@inproceedings{kazakov-etal-2024-petkaz,
    title = "{P}et{K}az at {S}em{E}val-2024 Task 3: Advancing Emotion Classification with an {LLM} for Emotion-Cause Pair Extraction in Conversations",
    author = "Kazakov, Roman  and
      Petukhova, Kseniia  and
      Kochmar, Ekaterina",
    editor = {Ojha, Atul Kr.  and
      Do{\u{g}}ru{\"o}z, A. Seza  and
      Tayyar Madabushi, Harish  and
      Da San Martino, Giovanni  and
      Rosenthal, Sara  and
      Ros{\'a}, Aiala},
    booktitle = "Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024)",
    month = jun,
    year = "2024",
    address = "Mexico City, Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2024.semeval-1.164/",
    doi = "10.18653/v1/2024.semeval-1.164",
    pages = "1127--1134",
    abstract = "In this paper, we present our submission to the SemEval-2023 Task 3 {\textquotedblleft}The Competition of Multimodal Emotion Cause Analysis in Conversations{\textquotedblright}, focusing on extracting emotion-cause pairs from dialogs. Specifically, our approach relies on combining fine-tuned GPT-3.5 for emotion classification and using 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."
}