Cordyceps@LT-EDI: Patching Language-Specific Homophobia/Transphobia
  Classifiers with a Multilingual Understanding
Dean Ninalga
Detecting transphobia, homophobia, and various other forms of hate speech is
difficult. Signals can vary depending on factors such as language, culture,
geographical region, and the particular online platform. Here, we present a
joint multilingual (M-L) and language-specific (L-S) approach to homophobia and
transphobic hate speech detection (HSD). M-L models are needed to catch words,
phrases, and concepts that are less common or missing in a particular language
and subsequently overlooked by L-S models. Nonetheless, L-S models are better
situated to understand the cultural and linguistic context of the users who
typically write in a particular language. Here we construct a simple and
successful way to merge the M-L and L-S approaches through simple weight
interpolation in such a way that is interpretable and data-driven. We
demonstrate our system on task A of the 'Shared Task on Homophobia/Transphobia
Detection in social media comments' dataset for homophobia and transphobic HSD.
Our system achieves the best results in three of five languages and achieves a
0.997 macro average F1-score on Malayalam texts.