@inproceedings{numanoglu-etal-2024-empathify,
    title = "Empathify at {WASSA} 2024 Empathy and Personality Shared Task: Contextualizing Empathy with a {BERT}-Based Context-Aware Approach for Empathy Detection",
    author = {Numano{\u{g}}lu, Arda  and
      Ate{\c{s}}, S{\"u}leyman  and
      Cicekli, Nihan  and
      K{\"u}{\c{c}}{\"u}k, Dilek},
    editor = "De Clercq, Orph{\'e}e  and
      Barriere, Valentin  and
      Barnes, Jeremy  and
      Klinger, Roman  and
      Sedoc, Jo{\~a}o  and
      Tafreshi, Shabnam",
    booktitle = "Proceedings of the 14th Workshop on Computational Approaches to Subjectivity, Sentiment, {\&} Social Media Analysis",
    month = aug,
    year = "2024",
    address = "Bangkok, Thailand",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2024.wassa-1.33/",
    doi = "10.18653/v1/2024.wassa-1.33",
    pages = "393--398",
    abstract = "Empathy detection from textual data is a complex task that requires an understanding of both the content and context of the text. This study presents a BERT-based context-aware approach to enhance empathy detection in conversations and essays. We participated in the WASSA 2024 Shared Task, focusing on two tracks: empathy and emotion prediction in conversations (CONV-turn) and empathy and distress prediction in essays (EMP). Our approach leverages contextual information by incorporating related articles and emotional characteristics as additional inputs, using BERT-based Siamese (parallel) architecture. Our experiments demonstrated that using article summaries as context significantly improves performance, with the parallel BERT approach outperforming the traditional method of concatenating inputs with the {\textquoteleft}[SEP]{\textquoteleft} token. These findings highlight the importance of context-awareness in empathy detection and pave the way for future improvements in the sensitivity and accuracy of such systems."
}