Abstract
The increased expansion of abusive content on social media platforms negatively affects online users. Transphobic/homophobic content indicates hatred comments for lesbian, gay, transgender, or bisexual people. It leads to offensive speech and causes severe social problems that can make online platforms toxic and unpleasant to LGBT+people, endeavoring to eliminate equality, diversity, and inclusion. In this paper, we present our classification system; given comments, it predicts whether or not it contains any form of homophobia/transphobia with a Zero-Shot learning framework. Our system submission achieved 0.40, 0.85, 0.89 F1-score for Tamil and Tamil-English, English with (1st, 1st,8th) ranks respectively. We release our codebase here.
Cite
CITATION STYLE
Singh, M., & Motlicek, P. (2022). IDIAP Submission@LT-EDI-ACL2022: Homophobia/Transphobia Detection in social media comments. In LTEDI 2022 - 2nd Workshop on Language Technology for Equality, Diversity and Inclusion, Proceedings of the Workshop (pp. 356–361). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/2022.ltedi-1.55
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