Papers › IDIAP_TIET@LT-EDI-ACL2022 : Hope Speech Detection in Social Media using Contextualized...
IDIAP_TIET@LT-EDI-ACL2022 : Hope Speech Detection in Social Media using Contextualized BERT with Attention Mechanism
Deepanshu Khanna, Muskaan Singh, Petr Motlicek
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 .
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