@inproceedings{wang-etal-2022-nycu,
title = "{NYCU}{\_}{TWD}@{LT}-{EDI}-{ACL}2022: Ensemble Models with {VADER} and Contrastive Learning for Detecting Signs of Depression from Social Media",
author = "Wang, Wei-Yao and
Tang, Yu-Chien and
Du, Wei-Wei and
Peng, Wen-Chih",
editor = "Chakravarthi, Bharathi Raja and
Bharathi, B and
McCrae, John P and
Zarrouk, Manel and
Bali, Kalika and
Buitelaar, Paul",
booktitle = "Proceedings of the Second Workshop on Language Technology for Equality, Diversity and Inclusion",
month = may,
year = "2022",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.ltedi-1.15",
doi = "10.18653/v1/2022.ltedi-1.15",
pages = "136--139",
abstract = "This paper presents a state-of-the-art solution to the LT-EDI-ACL 2022 Task 4: \textit{Detecting Signs of Depression from Social Media Text}. The goal of this task is to detect the severity levels of depression of people from social media posts, where people often share their feelings on a daily basis. To detect the signs of depression, we propose a framework with pre-trained language models using rich information instead of training from scratch, gradient boosting and deep learning models for modeling various aspects, and supervised contrastive learning for the generalization ability. Moreover, ensemble techniques are also employed in consideration of the different advantages of each method. Experiments show that our framework achieves a 2nd prize ranking with a macro F1-score of 0.552, showing the effectiveness and robustness of our approach.",
}
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<abstract>This paper presents a state-of-the-art solution to the LT-EDI-ACL 2022 Task 4: Detecting Signs of Depression from Social Media Text. The goal of this task is to detect the severity levels of depression of people from social media posts, where people often share their feelings on a daily basis. To detect the signs of depression, we propose a framework with pre-trained language models using rich information instead of training from scratch, gradient boosting and deep learning models for modeling various aspects, and supervised contrastive learning for the generalization ability. Moreover, ensemble techniques are also employed in consideration of the different advantages of each method. Experiments show that our framework achieves a 2nd prize ranking with a macro F1-score of 0.552, showing the effectiveness and robustness of our approach.</abstract>
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%0 Conference Proceedings
%T NYCU_TWD@LT-EDI-ACL2022: Ensemble Models with VADER and Contrastive Learning for Detecting Signs of Depression from Social Media
%A Wang, Wei-Yao
%A Tang, Yu-Chien
%A Du, Wei-Wei
%A Peng, Wen-Chih
%Y Chakravarthi, Bharathi Raja
%Y Bharathi, B.
%Y McCrae, John P.
%Y Zarrouk, Manel
%Y Bali, Kalika
%Y Buitelaar, Paul
%S Proceedings of the Second Workshop on Language Technology for Equality, Diversity and Inclusion
%D 2022
%8 May
%I Association for Computational Linguistics
%C Dublin, Ireland
%F wang-etal-2022-nycu
%X This paper presents a state-of-the-art solution to the LT-EDI-ACL 2022 Task 4: Detecting Signs of Depression from Social Media Text. The goal of this task is to detect the severity levels of depression of people from social media posts, where people often share their feelings on a daily basis. To detect the signs of depression, we propose a framework with pre-trained language models using rich information instead of training from scratch, gradient boosting and deep learning models for modeling various aspects, and supervised contrastive learning for the generalization ability. Moreover, ensemble techniques are also employed in consideration of the different advantages of each method. Experiments show that our framework achieves a 2nd prize ranking with a macro F1-score of 0.552, showing the effectiveness and robustness of our approach.
%R 10.18653/v1/2022.ltedi-1.15
%U https://aclanthology.org/2022.ltedi-1.15
%U https://doi.org/10.18653/v1/2022.ltedi-1.15
%P 136-139
Markdown (Informal)
[NYCU_TWD@LT-EDI-ACL2022: Ensemble Models with VADER and Contrastive Learning for Detecting Signs of Depression from Social Media](https://aclanthology.org/2022.ltedi-1.15) (Wang et al., LTEDI 2022)
ACL