@inproceedings{garcia-etal-2023-deeplearningbrasil,
title = "{D}eep{L}earning{B}rasil@{LT}-{EDI}-2023: Exploring Deep Learning Techniques for Detecting Depression in Social Media Text",
author = "Garcia, Eduardo and
Gomes, Juliana and
Barbosa Junior, Adalberto Ferreira and
Borges, Cardeque Henrique Bittes de Alvarenga and
da Silva, Nadia F{\'e}lix Felipe",
editor = "Chakravarthi, Bharathi R. and
Bharathi, B. and
Griffith, Joephine and
Bali, Kalika and
Buitelaar, Paul",
booktitle = "Proceedings of the Third Workshop on Language Technology for Equality, Diversity and Inclusion",
month = sep,
year = "2023",
address = "Varna, Bulgaria",
publisher = "INCOMA Ltd., Shoumen, Bulgaria",
url = "https://aclanthology.org/2023.ltedi-1.42",
pages = "272--278",
abstract = "In this paper, we delineate the strategy employed by our team, DeepLearningBrasil, which secured us the first place in the shared task DepSign-LT-EDI@RANLP-2023 with the advantage of 2.4{\%}. The task was to classify social media texts into three distinct levels of depression - {``}not depressed,{''} {``}moderately depressed,{''} and {``}severely depressed.{''} Leveraging the power of the RoBERTa and DeBERTa models, we further pre-trained them on a collected Reddit dataset, specifically curated from mental health-related Reddit{'}s communities (Subreddits), leading to an enhanced understanding of nuanced mental health discourse. To address lengthy textual data, we introduced truncation techniques that retained the essence of the content by focusing on its beginnings and endings. Our model was robust against unbalanced data by incorporating sample weights into the loss. Cross-validation and ensemble techniques were then employed to combine our k-fold trained models, delivering an optimal solution. The accompanying code is made available for transparency and further development.",
}
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<abstract>In this paper, we delineate the strategy employed by our team, DeepLearningBrasil, which secured us the first place in the shared task DepSign-LT-EDI@RANLP-2023 with the advantage of 2.4%. The task was to classify social media texts into three distinct levels of depression - “not depressed,” “moderately depressed,” and “severely depressed.” Leveraging the power of the RoBERTa and DeBERTa models, we further pre-trained them on a collected Reddit dataset, specifically curated from mental health-related Reddit’s communities (Subreddits), leading to an enhanced understanding of nuanced mental health discourse. To address lengthy textual data, we introduced truncation techniques that retained the essence of the content by focusing on its beginnings and endings. Our model was robust against unbalanced data by incorporating sample weights into the loss. Cross-validation and ensemble techniques were then employed to combine our k-fold trained models, delivering an optimal solution. The accompanying code is made available for transparency and further development.</abstract>
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%0 Conference Proceedings
%T DeepLearningBrasil@LT-EDI-2023: Exploring Deep Learning Techniques for Detecting Depression in Social Media Text
%A Garcia, Eduardo
%A Gomes, Juliana
%A Barbosa Junior, Adalberto Ferreira
%A Borges, Cardeque Henrique Bittes de Alvarenga
%A da Silva, Nadia Félix Felipe
%Y Chakravarthi, Bharathi R.
%Y Bharathi, B.
%Y Griffith, Joephine
%Y Bali, Kalika
%Y Buitelaar, Paul
%S Proceedings of the Third Workshop on Language Technology for Equality, Diversity and Inclusion
%D 2023
%8 September
%I INCOMA Ltd., Shoumen, Bulgaria
%C Varna, Bulgaria
%F garcia-etal-2023-deeplearningbrasil
%X In this paper, we delineate the strategy employed by our team, DeepLearningBrasil, which secured us the first place in the shared task DepSign-LT-EDI@RANLP-2023 with the advantage of 2.4%. The task was to classify social media texts into three distinct levels of depression - “not depressed,” “moderately depressed,” and “severely depressed.” Leveraging the power of the RoBERTa and DeBERTa models, we further pre-trained them on a collected Reddit dataset, specifically curated from mental health-related Reddit’s communities (Subreddits), leading to an enhanced understanding of nuanced mental health discourse. To address lengthy textual data, we introduced truncation techniques that retained the essence of the content by focusing on its beginnings and endings. Our model was robust against unbalanced data by incorporating sample weights into the loss. Cross-validation and ensemble techniques were then employed to combine our k-fold trained models, delivering an optimal solution. The accompanying code is made available for transparency and further development.
%U https://aclanthology.org/2023.ltedi-1.42
%P 272-278
Markdown (Informal)
[DeepLearningBrasil@LT-EDI-2023: Exploring Deep Learning Techniques for Detecting Depression in Social Media Text](https://aclanthology.org/2023.ltedi-1.42) (Garcia et al., LTEDI-WS 2023)
ACL
- Eduardo Garcia, Juliana Gomes, Adalberto Ferreira Barbosa Junior, Cardeque Henrique Bittes de Alvarenga Borges, and Nadia Félix Felipe da Silva. 2023. DeepLearningBrasil@LT-EDI-2023: Exploring Deep Learning Techniques for Detecting Depression in Social Media Text. In Proceedings of the Third Workshop on Language Technology for Equality, Diversity and Inclusion, pages 272–278, Varna, Bulgaria. INCOMA Ltd., Shoumen, Bulgaria.