@inproceedings{vazquez-osorio-etal-2024-pcicunam,
title = "{PCICUNAM} at {WASSA} 2024: Cross-lingual Emotion Detection Task with Hierarchical Classification and Weighted Loss Functions",
author = "V{\'a}zquez-Osorio, Jes{\'u}s and
Sierra, Gerardo and
G{\'o}mez-Adorno, Helena and
Bel-Enguix, Gemma",
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-1.48",
pages = "490--494",
abstract = "This paper addresses the shared task of multi-lingual emotion detection in tweets, presented at the Workshop on Computational Approaches to Subjectivity, Sentiment, and Social Media Analysis (WASSA) co-located with the ACL 2024 conference. The task involves predicting emotions from six classes in tweets from five different languages using only English for model training. Our approach focuses on addressing class imbalance through data augmentation, hierarchical classification, and the application of focal loss and weighted cross-entropy loss functions. These methods enhance our transformer-based model{'}s ability to transfer emotion detection capabilities across languages, resulting in improved performance despite the constraints of limited computational resources.",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="vazquez-osorio-etal-2024-pcicunam">
<titleInfo>
<title>PCICUNAM at WASSA 2024: Cross-lingual Emotion Detection Task with Hierarchical Classification and Weighted Loss Functions</title>
</titleInfo>
<name type="personal">
<namePart type="given">Jesús</namePart>
<namePart type="family">Vázquez-Osorio</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Gerardo</namePart>
<namePart type="family">Sierra</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Helena</namePart>
<namePart type="family">Gómez-Adorno</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Gemma</namePart>
<namePart type="family">Bel-Enguix</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>This paper addresses the shared task of multi-lingual emotion detection in tweets, presented at the Workshop on Computational Approaches to Subjectivity, Sentiment, and Social Media Analysis (WASSA) co-located with the ACL 2024 conference. The task involves predicting emotions from six classes in tweets from five different languages using only English for model training. Our approach focuses on addressing class imbalance through data augmentation, hierarchical classification, and the application of focal loss and weighted cross-entropy loss functions. These methods enhance our transformer-based model’s ability to transfer emotion detection capabilities across languages, resulting in improved performance despite the constraints of limited computational resources.</abstract>
<identifier type="citekey">vazquez-osorio-etal-2024-pcicunam</identifier>
<location>
<url>https://aclanthology.org/2024.wassa-1.48</url>
</location>
<part>
<date>2024-08</date>
<extent unit="page">
<start>490</start>
<end>494</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T PCICUNAM at WASSA 2024: Cross-lingual Emotion Detection Task with Hierarchical Classification and Weighted Loss Functions
%A Vázquez-Osorio, Jesús
%A Sierra, Gerardo
%A Gómez-Adorno, Helena
%A Bel-Enguix, Gemma
%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 vazquez-osorio-etal-2024-pcicunam
%X This paper addresses the shared task of multi-lingual emotion detection in tweets, presented at the Workshop on Computational Approaches to Subjectivity, Sentiment, and Social Media Analysis (WASSA) co-located with the ACL 2024 conference. The task involves predicting emotions from six classes in tweets from five different languages using only English for model training. Our approach focuses on addressing class imbalance through data augmentation, hierarchical classification, and the application of focal loss and weighted cross-entropy loss functions. These methods enhance our transformer-based model’s ability to transfer emotion detection capabilities across languages, resulting in improved performance despite the constraints of limited computational resources.
%U https://aclanthology.org/2024.wassa-1.48
%P 490-494
Markdown (Informal)
[PCICUNAM at WASSA 2024: Cross-lingual Emotion Detection Task with Hierarchical Classification and Weighted Loss Functions](https://aclanthology.org/2024.wassa-1.48) (Vázquez-Osorio et al., WASSA-WS 2024)
ACL