IDIAP_TIET@LT-EDI-ACL2022 : Hope Speech Detection in Social Media using Contextualized BERT with Attention Mechanism

Deepanshu Khanna, Muskaan Singh, Petr Motlicek


Abstract
With the increase of users on social media platforms, manipulating or provoking masses of people has become a piece of cake. This spread of hatred among people, which has become a loophole for freedom of speech, must be minimized. Hence, it is essential to have a system that automatically classifies the hatred content, especially on social media, to take it down. This paper presents a simple modular pipeline classifier with BERT embeddings and attention mechanism to classify hope speech content in the Hope Speech Detection shared task for Equality, Diversity, and Inclusion-ACL 2022. Our system submission ranks fourth with an F1-score of 0.84. We release our code-base here https://github.com/Deepanshu-beep/hope-speech-attention .
Anthology ID:
2022.ltedi-1.49
Volume:
Proceedings of the Second Workshop on Language Technology for Equality, Diversity and Inclusion
Month:
May
Year:
2022
Address:
Dublin, Ireland
Editors:
Bharathi Raja Chakravarthi, B Bharathi, John P McCrae, Manel Zarrouk, Kalika Bali, Paul Buitelaar
Venue:
LTEDI
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
321–325
Language:
URL:
https://aclanthology.org/2022.ltedi-1.49
DOI:
10.18653/v1/2022.ltedi-1.49
Bibkey:
Cite (ACL):
Deepanshu Khanna, Muskaan Singh, and Petr Motlicek. 2022. IDIAP_TIET@LT-EDI-ACL2022 : Hope Speech Detection in Social Media using Contextualized BERT with Attention Mechanism. In Proceedings of the Second Workshop on Language Technology for Equality, Diversity and Inclusion, pages 321–325, Dublin, Ireland. Association for Computational Linguistics.
Cite (Informal):
IDIAP_TIET@LT-EDI-ACL2022 : Hope Speech Detection in Social Media using Contextualized BERT with Attention Mechanism (Khanna et al., LTEDI 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.ltedi-1.49.pdf
Video:
 https://aclanthology.org/2022.ltedi-1.49.mp4
Code
 deepanshu-beep/hope-speech-attention