@inproceedings{frick-steinebach-2024-fraunhofer,
title = "Fraunhofer {SIT} at {WASSA} 2024 Empathy and Personality Shared Task: Use of Sentiment Transformers and Data Augmentation With Fuzzy Labels to Predict Emotional Reactions in Conversations and Essays",
author = "Frick, Raphael and
Steinebach, Martin",
editor = "De Clercq, Orph{\'e}e and
Barriere, Valentin and
Barnes, Jeremy and
Klinger, Roman and
Sedoc, Jo{\~a}o and
Tafreshi, Shabnam",
booktitle = "Proceedings of the 14th Workshop on Computational Approaches to Subjectivity, Sentiment, {\&} Social Media Analysis",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.wassa-1.40",
pages = "435--440",
abstract = "Predicting emotions and emotional reactions during conversations and within texts poses challenges, even for advanced AI systems. The second iteration of the WASSA Empathy and Personality Shared Task focuses on creating innovative models that can anticipate emotional responses to news articles containing harmful content across four tasks.In this paper, we introduce our Fraunhofer SIT team{'}s solutions for the three tasks: Task 1 (CONVD), Task 2 (CONVT), and Task 3 (EMP).It involves combining LLM-driven data augmentation with fuzzy labels and fine-tuning RoBERTa models pre-trained on sentiment classification tasks to solve the regression problems. In the competition, our solutions achieved first place in Task 1, X in Task 2, and third place in Task 3.",
}
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<abstract>Predicting emotions and emotional reactions during conversations and within texts poses challenges, even for advanced AI systems. The second iteration of the WASSA Empathy and Personality Shared Task focuses on creating innovative models that can anticipate emotional responses to news articles containing harmful content across four tasks.In this paper, we introduce our Fraunhofer SIT team’s solutions for the three tasks: Task 1 (CONVD), Task 2 (CONVT), and Task 3 (EMP).It involves combining LLM-driven data augmentation with fuzzy labels and fine-tuning RoBERTa models pre-trained on sentiment classification tasks to solve the regression problems. In the competition, our solutions achieved first place in Task 1, X in Task 2, and third place in Task 3.</abstract>
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%0 Conference Proceedings
%T Fraunhofer SIT at WASSA 2024 Empathy and Personality Shared Task: Use of Sentiment Transformers and Data Augmentation With Fuzzy Labels to Predict Emotional Reactions in Conversations and Essays
%A Frick, Raphael
%A Steinebach, Martin
%Y De Clercq, Orphée
%Y Barriere, Valentin
%Y Barnes, Jeremy
%Y Klinger, Roman
%Y Sedoc, João
%Y Tafreshi, Shabnam
%S Proceedings of the 14th Workshop on Computational Approaches to Subjectivity, Sentiment, & Social Media Analysis
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand
%F frick-steinebach-2024-fraunhofer
%X Predicting emotions and emotional reactions during conversations and within texts poses challenges, even for advanced AI systems. The second iteration of the WASSA Empathy and Personality Shared Task focuses on creating innovative models that can anticipate emotional responses to news articles containing harmful content across four tasks.In this paper, we introduce our Fraunhofer SIT team’s solutions for the three tasks: Task 1 (CONVD), Task 2 (CONVT), and Task 3 (EMP).It involves combining LLM-driven data augmentation with fuzzy labels and fine-tuning RoBERTa models pre-trained on sentiment classification tasks to solve the regression problems. In the competition, our solutions achieved first place in Task 1, X in Task 2, and third place in Task 3.
%U https://aclanthology.org/2024.wassa-1.40
%P 435-440
Markdown (Informal)
[Fraunhofer SIT at WASSA 2024 Empathy and Personality Shared Task: Use of Sentiment Transformers and Data Augmentation With Fuzzy Labels to Predict Emotional Reactions in Conversations and Essays](https://aclanthology.org/2024.wassa-1.40) (Frick & Steinebach, WASSA-WS 2024)
ACL