NYCU TWD@LT-EDI-ACL2022: Ensemble Models with VADER and Contrastive Learning for Detecting Signs of Depression from Social Media

Wei Yao Wang, Yu Chien Tang, Wei Wei Du, Wen Chih Peng

研究成果: Conference contribution同行評審

15 引文 斯高帕斯(Scopus)

摘要

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.

原文English
主出版物標題LTEDI 2022 - 2nd Workshop on Language Technology for Equality, Diversity and Inclusion, Proceedings of the Workshop
編輯Bharathi Raja Chakravarthi, B Bharathi, John P McCrae, Manel Zarrouk, Kalika Bali, Paul Buitelaar
發行者Association for Computational Linguistics (ACL)
頁面136-139
頁數4
ISBN(電子)9781955917438
出版狀態Published - 2022
事件2nd Workshop on Language Technology for Equality, Diversity and Inclusion, LTEDI 2022 - Dublin, Ireland
持續時間: 27 5月 2022 → …

出版系列

名字LTEDI 2022 - 2nd Workshop on Language Technology for Equality, Diversity and Inclusion, Proceedings of the Workshop

Conference

Conference2nd Workshop on Language Technology for Equality, Diversity and Inclusion, LTEDI 2022
國家/地區Ireland
城市Dublin
期間27/05/22 → …

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