@inproceedings{srinivas-etal-2023-team,
    title = "{T}eam{\_}{H}awk at {WASSA} 2023 Empathy, Emotion, and Personality Shared Task: Multi-tasking Multi-encoder based transformers for Empathy and Emotion Prediction in Conversations",
    author = "Srinivas, Addepalli Sai  and
      Barua, Nabarun  and
      Pal, Santanu",
    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-1.48",
    doi = "10.18653/v1/2023.wassa-1.48",
    pages = "542--547",
    abstract = "In this paper, we present Team Hawk{'}s participation in Track 1 of the WASSA 2023 shared task. The objective of the task is to understand the empathy that emerges between individuals during their conversations. In our study, we developed a multi-tasking framework that is capable of automatically assessing empathy, intensity of emotion, and polarity of emotion within participants{'} conversations. Our proposed core model extends the transformer architecture, utilizing two separate RoBERTa-based encoders to encode both the articles and conversations. Subsequently, a sequence of self-attention, position-wise feed-forward, and dense layers are employed to predict the regression scores for the three sub-tasks: empathy, intensity of emotion, and polarity of emotion. Our best model achieved average Pearson{'}s correlation of 0.7710 (Empathy: 0.7843, Emotion Polarity: 0.7917, Emotion Intensity: 0.7381) on the released development set and 0.7250 (Empathy: 0.8090, Emotion Polarity: 0.7010, Emotion Intensity: 0.6650) on the released test set. These results earned us the 3rd position in the test set evaluation phase of Track 1.",
}

In this paper, we present Team Hawk{'}s participation in Track 1 of the WASSA 2023 shared task. The objective of the task is to understand the empathy that emerges between individuals during their conversations. In our study, we developed a multi-tasking framework that is capable of automatically assessing empathy, intensity of emotion, and polarity of emotion within participants{'} conversations. Our proposed core model extends the transformer architecture, utilizing two separate RoBERTa-based encoders to encode both the articles and conversations. Subsequently, a sequence of self-attention, position-wise feed-forward, and dense layers are employed to predict the regression scores for the three sub-tasks: empathy, intensity of emotion, and polarity of emotion. Our best model achieved average Pearson{'}s correlation of 0.7710 (Empathy: 0.7843, Emotion Polarity: 0.7917, Emotion Intensity: 0.7381) on the released development set and 0.7250 (Empathy: 0.8090, Emotion Polarity: 0.7010, Emotion Intensity: 0.6650) on the released test set. These results earned us the 3rd position in the test set evaluation phase of Track 1.
Srinivas, Addepalli Sai Barua, Nabarun Pal, Santanu
Team_Hawk at WASSA 2023 Empathy, Emotion, and Personality Shared Task: Multi-tasking Multi-encoder based transformers for Empathy and Emotion Prediction in Conversations