SSN_MLRG3 @LT-EDI-ACL2022-Depression Detection System from Social Media Text using Transformer Models

Sarika Esackimuthu, Shruthi Hariprasad, Rajalakshmi Sivanaiah, Angel S, Sakaya Milton Rajendram, Mirnalinee T T


Abstract
Depression is a common mental illness that involves sadness and lack of interest in all day-to-day activities. The task is to classify the social media text as signs of depression into three labels namely “not depressed”, “moderately depressed”, and “severely depressed”. We have build a system using Deep Learning Model “Transformers”. Transformers provides thousands of pretrained models to perform tasks on different modalities such as text, vision, and audio. The multi-class classification model used in our system is based on the ALBERT model. In the shared task ACL 2022, Our team SSN_MLRG3 obtained a Macro F1 score of 0.473.
Anthology ID:
2022.ltedi-1.26
Volume:
Proceedings of the Second Workshop on Language Technology for Equality, Diversity and Inclusion
Month:
May
Year:
2022
Address:
Dublin, Ireland
Venues:
ACL | LTEDI
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
196–199
Language:
URL:
https://aclanthology.org/2022.ltedi-1.26
DOI:
10.18653/v1/2022.ltedi-1.26
Bibkey:
Cite (ACL):
Sarika Esackimuthu, Shruthi Hariprasad, Rajalakshmi Sivanaiah, Angel S, Sakaya Milton Rajendram, and Mirnalinee T T. 2022. SSN_MLRG3 @LT-EDI-ACL2022-Depression Detection System from Social Media Text using Transformer Models. In Proceedings of the Second Workshop on Language Technology for Equality, Diversity and Inclusion, pages 196–199, Dublin, Ireland. Association for Computational Linguistics.
Cite (Informal):
SSN_MLRG3 @LT-EDI-ACL2022-Depression Detection System from Social Media Text using Transformer Models (Esackimuthu et al., LTEDI 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.ltedi-1.26.pdf