@inproceedings{levchenko-etal-2024-pweitinlp,
    title = "{PWEITINLP} at {S}em{E}val-2024 Task 3: Two Step Emotion Cause Analysis",
    author = "Levchenko, Sofiia  and
      Wolert, Rafa{\l}  and
      Andruszkiewicz, Piotr",
    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-1.159/",
    doi = "10.18653/v1/2024.semeval-1.159",
    pages = "1097--1105",
    abstract = "ECPE (emotion cause pair extraction) task was introduced to solve the shortcomings of ECE (emotion cause extraction). Models with sequential data processing abilities or complex architecture can be utilized to solve this task. Our contribution to solving Subtask 1: Textual Emotion-Cause Pair Extraction in Conversations defined in the SemEval-2024 Task 3: The Competition of Multimodal Emotion Cause Analysis in Conversations is to create a two-step solution to the ECPE task utilizing GPT-3 for emotion classification and SpanBERT for extracting the cause utterances."
}