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.

通过多语言和特定语言的联合方法，本研究构建了一种简单而成功的方法来检测针对跨性别和同性恋群体的仇恨言论，并在多种语言中取得了最佳结果，达到马拉雅拉姆语文本方面的 0.997 宏平均 F1 分数。