@inproceedings{paranjape-etal-2023-converge,
    title = "Converge at {WASSA} 2023 Empathy, Emotion and Personality Shared Task: A Transformer-based Approach for Multi-Label Emotion Classification",
    author = "Paranjape, Aditya  and
      Kolhatkar, Gaurav  and
      Patwardhan, Yash  and
      Gokhale, Omkar  and
      Dharmadhikari, Shweta",
    editor = "Barnes, Jeremy  and
      De Clercq, Orph{\'e}e  and
      Klinger, Roman",
    booktitle = "Proceedings of the 13th Workshop on Computational Approaches to Subjectivity, Sentiment, {\&} Social Media Analysis",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.wassa-1.51",
    doi = "10.18653/v1/2023.wassa-1.51",
    pages = "558--563",
    abstract = "In this paper, we highlight our approach for the {``}WASSA 2023 Shared-Task 1: Empathy Detection and Emotion Classification{''}. By accurately identifying emotions from textual sources of data, deep learning models can be trained to understand and interpret human emotions more effectively. The classification of emotions facilitates the creation of more emotionally intelligent systems that can better understand and respond to human emotions. We compared multiple transformer-based models for multi-label classification. Ensembling and oversampling were used to improve the performance of the system. A threshold-based voting mechanism performed on three models (Longformer, BERT, BigBird) yields the highest overall macro F1-score of 0.6605.",
}

In this paper, we highlight our approach for the {``}WASSA 2023 Shared-Task 1: Empathy Detection and Emotion Classification{''}. By accurately identifying emotions from textual sources of data, deep learning models can be trained to understand and interpret human emotions more effectively. The classification of emotions facilitates the creation of more emotionally intelligent systems that can better understand and respond to human emotions. We compared multiple transformer-based models for multi-label classification. Ensembling and oversampling were used to improve the performance of the system. A threshold-based voting mechanism performed on three models (Longformer, BERT, BigBird) yields the highest overall macro F1-score of 0.6605.
Paranjape, Aditya Kolhatkar, Gaurav Patwardhan, Yash Gokhale, Omkar Dharmadhikari, Shweta
Converge at WASSA 2023 Empathy, Emotion and Personality Shared Task: A Transformer-based Approach for Multi-Label Emotion Classification