@inproceedings{gruschka-etal-2023-domain,
title = "Domain Transfer for Empathy, Distress, and Personality Prediction",
author = "Gruschka, Fabio and
Lahnala, Allison and
Welch, Charles and
Flek, Lucie",
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-1.50",
doi = "10.18653/v1/2023.wassa-1.50",
pages = "553--557",
abstract = "This research contributes to the task of predicting empathy and personality traits within dialogue, an important aspect of natural language processing, as part of our experimental work for the WASSA 2023 Empathy and Emotion Shared Task. For predicting empathy, emotion polarity, and emotion intensity on turns within a dialogue, we employ adapters trained on social media interactions labeled with empathy ratings in a stacked composition with the target task adapters. Furthermore, we embed demographic information to predict Interpersonal Reactivity Index (IRI) subscales and Big Five Personality Traits utilizing BERT-based models. The results from our study provide valuable insights, contributing to advancements in understanding human behavior and interaction through text. Our team ranked 2nd on the personality and empathy prediction tasks, 4th on the interpersonal reactivity index, and 6th on the conversational task.",
}
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<abstract>This research contributes to the task of predicting empathy and personality traits within dialogue, an important aspect of natural language processing, as part of our experimental work for the WASSA 2023 Empathy and Emotion Shared Task. For predicting empathy, emotion polarity, and emotion intensity on turns within a dialogue, we employ adapters trained on social media interactions labeled with empathy ratings in a stacked composition with the target task adapters. Furthermore, we embed demographic information to predict Interpersonal Reactivity Index (IRI) subscales and Big Five Personality Traits utilizing BERT-based models. The results from our study provide valuable insights, contributing to advancements in understanding human behavior and interaction through text. Our team ranked 2nd on the personality and empathy prediction tasks, 4th on the interpersonal reactivity index, and 6th on the conversational task.</abstract>
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%0 Conference Proceedings
%T Domain Transfer for Empathy, Distress, and Personality Prediction
%A Gruschka, Fabio
%A Lahnala, Allison
%A Welch, Charles
%A Flek, Lucie
%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 gruschka-etal-2023-domain
%X This research contributes to the task of predicting empathy and personality traits within dialogue, an important aspect of natural language processing, as part of our experimental work for the WASSA 2023 Empathy and Emotion Shared Task. For predicting empathy, emotion polarity, and emotion intensity on turns within a dialogue, we employ adapters trained on social media interactions labeled with empathy ratings in a stacked composition with the target task adapters. Furthermore, we embed demographic information to predict Interpersonal Reactivity Index (IRI) subscales and Big Five Personality Traits utilizing BERT-based models. The results from our study provide valuable insights, contributing to advancements in understanding human behavior and interaction through text. Our team ranked 2nd on the personality and empathy prediction tasks, 4th on the interpersonal reactivity index, and 6th on the conversational task.
%R 10.18653/v1/2023.wassa-1.50
%U https://aclanthology.org/2023.wassa-1.50
%U https://doi.org/10.18653/v1/2023.wassa-1.50
%P 553-557
Markdown (Informal)
[Domain Transfer for Empathy, Distress, and Personality Prediction](https://aclanthology.org/2023.wassa-1.50) (Gruschka et al., WASSA 2023)
ACL
- Fabio Gruschka, Allison Lahnala, Charles Welch, and Lucie Flek. 2023. Domain Transfer for Empathy, Distress, and Personality Prediction. In Proceedings of the 13th Workshop on Computational Approaches to Subjectivity, Sentiment, & Social Media Analysis, pages 553–557, Toronto, Canada. Association for Computational Linguistics.