%0 Conference Proceedings
%T IDIAP_TIET@LT-EDI-ACL2022 : Hope Speech Detection in Social Media using Contextualized BERT with Attention Mechanism
%A Khanna, Deepanshu
%A Singh, Muskaan
%A Motlicek, Petr
%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 khanna-etal-2022-idiap
%X 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 .
%R 10.18653/v1/2022.ltedi-1.49
%U https://aclanthology.org/2022.ltedi-1.49
%U https://doi.org/10.18653/v1/2022.ltedi-1.49
%P 321-325