@inproceedings{huang-liang-2024-zhenmei,
title = "Zhenmei at {WASSA}-2024 Empathy and Personality Shared Track 2 Incorporating {P}earson Correlation Coefficient as a Regularization Term for Enhanced Empathy and Emotion Prediction in Conversational Turns",
author = "Huang, Liting and
Liang, Huizhi",
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-1.34",
pages = "399--403",
abstract = "In the realm of conversational empathy and emotion prediction, emotions are frequently categorized into multiple levels. This study seeks to enhance the performance of emotion prediction models by incorporating the Pearson correlation coefficient as a regularization term within the loss function. This regularization approach ensures closer alignment between predicted and actual emotion levels, mitigating extreme predictions and resulting in smoother and more consistent outputs. Such outputs are essential for capturing the subtle transitions between continuous emotion levels. Through experimental comparisons between models with and without Pearson regularization, our findings demonstrate that integrating the Pearson correlation coefficient significantly boosts model performance, yielding higher correlation scores and more accurate predictions. Our system officially ranked 9th at the Track 2: CONV-turn. The code for our model can be found at Link .",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="huang-liang-2024-zhenmei">
<titleInfo>
<title>Zhenmei at WASSA-2024 Empathy and Personality Shared Track 2 Incorporating Pearson Correlation Coefficient as a Regularization Term for Enhanced Empathy and Emotion Prediction in Conversational Turns</title>
</titleInfo>
<name type="personal">
<namePart type="given">Liting</namePart>
<namePart type="family">Huang</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Huizhi</namePart>
<namePart type="family">Liang</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2024-08</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the 14th Workshop on Computational Approaches to Subjectivity, Sentiment, & Social Media Analysis</title>
</titleInfo>
<name type="personal">
<namePart type="given">Orphée</namePart>
<namePart type="family">De Clercq</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Valentin</namePart>
<namePart type="family">Barriere</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Jeremy</namePart>
<namePart type="family">Barnes</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Roman</namePart>
<namePart type="family">Klinger</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">João</namePart>
<namePart type="family">Sedoc</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Shabnam</namePart>
<namePart type="family">Tafreshi</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Bangkok, Thailand</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>In the realm of conversational empathy and emotion prediction, emotions are frequently categorized into multiple levels. This study seeks to enhance the performance of emotion prediction models by incorporating the Pearson correlation coefficient as a regularization term within the loss function. This regularization approach ensures closer alignment between predicted and actual emotion levels, mitigating extreme predictions and resulting in smoother and more consistent outputs. Such outputs are essential for capturing the subtle transitions between continuous emotion levels. Through experimental comparisons between models with and without Pearson regularization, our findings demonstrate that integrating the Pearson correlation coefficient significantly boosts model performance, yielding higher correlation scores and more accurate predictions. Our system officially ranked 9th at the Track 2: CONV-turn. The code for our model can be found at Link .</abstract>
<identifier type="citekey">huang-liang-2024-zhenmei</identifier>
<location>
<url>https://aclanthology.org/2024.wassa-1.34</url>
</location>
<part>
<date>2024-08</date>
<extent unit="page">
<start>399</start>
<end>403</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Zhenmei at WASSA-2024 Empathy and Personality Shared Track 2 Incorporating Pearson Correlation Coefficient as a Regularization Term for Enhanced Empathy and Emotion Prediction in Conversational Turns
%A Huang, Liting
%A Liang, Huizhi
%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 huang-liang-2024-zhenmei
%X In the realm of conversational empathy and emotion prediction, emotions are frequently categorized into multiple levels. This study seeks to enhance the performance of emotion prediction models by incorporating the Pearson correlation coefficient as a regularization term within the loss function. This regularization approach ensures closer alignment between predicted and actual emotion levels, mitigating extreme predictions and resulting in smoother and more consistent outputs. Such outputs are essential for capturing the subtle transitions between continuous emotion levels. Through experimental comparisons between models with and without Pearson regularization, our findings demonstrate that integrating the Pearson correlation coefficient significantly boosts model performance, yielding higher correlation scores and more accurate predictions. Our system officially ranked 9th at the Track 2: CONV-turn. The code for our model can be found at Link .
%U https://aclanthology.org/2024.wassa-1.34
%P 399-403
Markdown (Informal)
[Zhenmei at WASSA-2024 Empathy and Personality Shared Track 2 Incorporating Pearson Correlation Coefficient as a Regularization Term for Enhanced Empathy and Emotion Prediction in Conversational Turns](https://aclanthology.org/2024.wassa-1.34) (Huang & Liang, WASSA-WS 2024)
ACL