@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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Team_Hawk at WASSA 2023 Empathy, Emotion, and Personality Shared Task: Multi-tasking Multi-encoder based transformers for Empathy and Emotion Prediction in Conversations
%A Srinivas, Addepalli Sai
%A Barua, Nabarun
%A Pal, Santanu
%Y Barnes, Jeremy
%Y De Clercq, Orphée
%Y Klinger, Roman
%S Proceedings of the 13th Workshop on Computational Approaches to Subjectivity, Sentiment, & Social Media Analysis
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F srinivas-etal-2023-team
%X 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.
%R 10.18653/v1/2023.wassa-1.48
%U https://aclanthology.org/2023.wassa-1.48
%U https://doi.org/10.18653/v1/2023.wassa-1.48
%P 542-547
Markdown (Informal)
[Team_Hawk at WASSA 2023 Empathy, Emotion, and Personality Shared Task: Multi-tasking Multi-encoder based transformers for Empathy and Emotion Prediction in Conversations](https://aclanthology.org/2023.wassa-1.48) (Srinivas et al., WASSA 2023)
ACL