Cordyceps@ LT - EDI : Patching Language-Specific Homophobia/Transphobia Classifiers with a Multilingual Understanding (2023)

Abstract: 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.

## introduction in general, the us is seeing an increase in institutionalized transphobia in the form of banning gender-affirming care and the banning of transgender youth from several sports @xcite . however, studies on individuals who experience institutionalized transphobia in the us experience more psychological distress and instances of suicidal ideation @xcite . the codifying of anti-trans laws then certainly must give confidence to those with transphobic beliefs and desires to spread anti-trans rhetoric in online spaces. @xcite recently presented results showing that lgbtq youth often rely on social media for improved mental health outcomes and as a source of social connection that helps close mental health disparities. therefore, appropriate content moderation on social media platforms stands to benefit from accurate nlp systems that can identify homophobia, transphobia, and other forms of hate speech. good knowledge of hate speech in a particular language may not always be useful for other languages, yet many common phrases and sayings are often expressed across languages. namely, purveyors of hate speech often do not openly say hateful comments but instead rely on equally vicious code phrases, or dogwhistles, to avoid existing content moderation systems @xcite . knowledge of the hidden meanings of these encoded sayings can create powerful tools for improving online moderation @xcite . these phrases can easily transcend the regions of their origin, spreading across online communities without detection in vulnerable communities. hence, knowledge of dogwhistles in their current form will make content moderation systems more robust to these signals as they appear in different languages in new online spaces. textual databases built for hate speech analysis are predominantly in english, which creates language-based performance disparities @xcite @xcite . as wang et al. (2020) suggested, in m-l models languages are in competing for model resources, potentially resulting in worse performance for low-resources languages. this performance bias is possibly due to that many m-l datasets used for pretraining popular language models often are majority english samples, often by a wide margin @xcite @xcite ). consequently, there is a general disparity in performance when comparing english-only and m-l hsd models @xcite . nozza et al. (2020) push for more pretrained models in non-english languages as they will (naturally) be best for downstream tasks in the same language domain they are trained in. however, pre-training techniques typically require large datasets to guarantee good downstream performance. given a relative lack of language-specific data for hsp, more indirect and creative approaches are required to alleviate the performance gap between english and non-english tasks. for our present purposes, we are presented with multiple target languages and tasked to detect levels of homophobia and transphobia for each specified language using an automated system. we introduce language-paint to jointly model m-l and l-s knowledge that incorporates recent work on weight interpolation. in summary, our main contributions are the following: ## methodology here, we use bernice @xcite , a language model exclusively on twitter foot_0 data and is known to be performant on hsd across multiple languages. indeed, many studies rely on twitter, to construct datasets of code-mixed samples for various hsd approaches @xcite @xcite , which in aggregate, motivates our choice of language model. ## conclusion in this paper, we introduce langpaint. lang-paint is a weight space ensembling strategy @xcite repurposed to jointly model the multi-lingual and language-specific