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    <abstract>Depression is a common and serious mental illness that early detection can improve the patient’s symptoms and make depression easier to treat. This paper mainly introduces the relevant content of the task “Detecting Signs of Depression from Social Media Text at DepSign-LT-EDI@ACL-2022”. The goal of DepSign is to classify the signs of depression into three labels namely “not depressed”, “moderately depressed”, and “severely depressed” based on social media’s posts. In this paper, we propose a predictive ensemble model that utilizes the fine-tuned contextualized word embedding, ALBERT, DistilBERT, RoBERTa, and BERT base model. We show that our model outperforms the baseline models in all considered metrics and achieves an F1 score of 54% and accuracy of 61%, ranking 5th on the leader-board for the DepSign task.</abstract>
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%0 Conference Proceedings
%T KADO@LT-EDI-ACL2022: BERT-based Ensembles for Detecting Signs of Depression from Social Media Text
%A Janatdoust, Morteza
%A Ehsani-Besheli, Fatemeh
%A Zeinali, Hossein
%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 janatdoust-etal-2022-kado
%X Depression is a common and serious mental illness that early detection can improve the patient’s symptoms and make depression easier to treat. This paper mainly introduces the relevant content of the task “Detecting Signs of Depression from Social Media Text at DepSign-LT-EDI@ACL-2022”. The goal of DepSign is to classify the signs of depression into three labels namely “not depressed”, “moderately depressed”, and “severely depressed” based on social media’s posts. In this paper, we propose a predictive ensemble model that utilizes the fine-tuned contextualized word embedding, ALBERT, DistilBERT, RoBERTa, and BERT base model. We show that our model outperforms the baseline models in all considered metrics and achieves an F1 score of 54% and accuracy of 61%, ranking 5th on the leader-board for the DepSign task.
%R 10.18653/v1/2022.ltedi-1.38
%U https://aclanthology.org/2022.ltedi-1.38
%U https://doi.org/10.18653/v1/2022.ltedi-1.38
%P 265-269

Markdown (Informal)

[KADO@LT-EDI-ACL2022: BERT-based Ensembles for Detecting Signs of Depression from Social Media Text](https://aclanthology.org/2022.ltedi-1.38) (Janatdoust et al., LTEDI 2022)

• KADO@LT-EDI-ACL2022: BERT-based Ensembles for Detecting Signs of Depression from Social Media Text (Janatdoust et al., LTEDI 2022)

ACL

• Morteza Janatdoust, Fatemeh Ehsani-Besheli, and Hossein Zeinali. 2022. KADO@LT-EDI-ACL2022: BERT-based Ensembles for Detecting Signs of Depression from Social Media Text. In Proceedings of the Second Workshop on Language Technology for Equality, Diversity and Inclusion, pages 265–269, Dublin, Ireland. Association for Computational Linguistics.