@inproceedings{mundra-etal-2021-wassa,
    title = "{WASSA}@{IITK} at {WASSA} 2021: Multi-task Learning and Transformer Finetuning for Emotion Classification and Empathy Prediction",
    author = "Mundra, Jay  and
      Gupta, Rohan  and
      Mukherjee, Sagnik",
    editor = "De Clercq, Orphee  and
      Balahur, Alexandra  and
      Sedoc, Joao  and
      Barriere, Valentin  and
      Tafreshi, Shabnam  and
      Buechel, Sven  and
      Hoste, Veronique",
    booktitle = "Proceedings of the Eleventh Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
    month = apr,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2021.wassa-1.12/",
    pages = "112--116",
    abstract = "This paper describes our contribution to the WASSA 2021 shared task on Empathy Prediction and Emotion Classification. The broad goal of this task was to model an empathy score, a distress score and the overall level of emotion of an essay written in response to a newspaper article associated with harm to someone. We have used the ELECTRA model abundantly and also advanced deep learning approaches like multi-task learning. Additionally, we also leveraged standard machine learning techniques like ensembling. Our system achieves a Pearson Correlation Coefficient of 0.533 on sub-task I and a macro F1 score of 0.5528 on sub-task II. We ranked 1st in Emotion Classification sub-task and 3rd in Empathy Prediction sub-task."
}