@inproceedings{paranjape-etal-2023-converge,
title = "Converge at {WASSA} 2023 Empathy, Emotion and Personality Shared Task: A Transformer-based Approach for Multi-Label Emotion Classification",
author = "Paranjape, Aditya and
Kolhatkar, Gaurav and
Patwardhan, Yash and
Gokhale, Omkar and
Dharmadhikari, Shweta",
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-1.51",
doi = "10.18653/v1/2023.wassa-1.51",
pages = "558--563",
abstract = "In this paper, we highlight our approach for the {``}WASSA 2023 Shared-Task 1: Empathy Detection and Emotion Classification{''}. By accurately identifying emotions from textual sources of data, deep learning models can be trained to understand and interpret human emotions more effectively. The classification of emotions facilitates the creation of more emotionally intelligent systems that can better understand and respond to human emotions. We compared multiple transformer-based models for multi-label classification. Ensembling and oversampling were used to improve the performance of the system. A threshold-based voting mechanism performed on three models (Longformer, BERT, BigBird) yields the highest overall macro F1-score of 0.6605.",
}
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<abstract>In this paper, we highlight our approach for the “WASSA 2023 Shared-Task 1: Empathy Detection and Emotion Classification”. By accurately identifying emotions from textual sources of data, deep learning models can be trained to understand and interpret human emotions more effectively. The classification of emotions facilitates the creation of more emotionally intelligent systems that can better understand and respond to human emotions. We compared multiple transformer-based models for multi-label classification. Ensembling and oversampling were used to improve the performance of the system. A threshold-based voting mechanism performed on three models (Longformer, BERT, BigBird) yields the highest overall macro F1-score of 0.6605.</abstract>
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%0 Conference Proceedings
%T Converge at WASSA 2023 Empathy, Emotion and Personality Shared Task: A Transformer-based Approach for Multi-Label Emotion Classification
%A Paranjape, Aditya
%A Kolhatkar, Gaurav
%A Patwardhan, Yash
%A Gokhale, Omkar
%A Dharmadhikari, Shweta
%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 paranjape-etal-2023-converge
%X In this paper, we highlight our approach for the “WASSA 2023 Shared-Task 1: Empathy Detection and Emotion Classification”. By accurately identifying emotions from textual sources of data, deep learning models can be trained to understand and interpret human emotions more effectively. The classification of emotions facilitates the creation of more emotionally intelligent systems that can better understand and respond to human emotions. We compared multiple transformer-based models for multi-label classification. Ensembling and oversampling were used to improve the performance of the system. A threshold-based voting mechanism performed on three models (Longformer, BERT, BigBird) yields the highest overall macro F1-score of 0.6605.
%R 10.18653/v1/2023.wassa-1.51
%U https://aclanthology.org/2023.wassa-1.51
%U https://doi.org/10.18653/v1/2023.wassa-1.51
%P 558-563
Markdown (Informal)
[Converge at WASSA 2023 Empathy, Emotion and Personality Shared Task: A Transformer-based Approach for Multi-Label Emotion Classification](https://aclanthology.org/2023.wassa-1.51) (Paranjape et al., WASSA 2023)
ACL
- Aditya Paranjape, Gaurav Kolhatkar, Yash Patwardhan, Omkar Gokhale, and Shweta Dharmadhikari. 2023. Converge at WASSA 2023 Empathy, Emotion and Personality Shared Task: A Transformer-based Approach for Multi-Label Emotion Classification. In Proceedings of the 13th Workshop on Computational Approaches to Subjectivity, Sentiment, & Social Media Analysis, pages 558–563, Toronto, Canada. Association for Computational Linguistics.