Cordyceps@LT-EDI：用多语言理解修补语言特定的恐同/恐跨分类器
Cordyceps@LT-EDI: Patching Language-Specific Homophobia/Transphobia Classifiers with a Multilingual Understanding
AI总结：
本文提出通过可解释的数据驱动权重插值融合多语言与语言特定模型，以提升社交媒体恐同/恐跨仇恨言论检测性能。
摘要：
检测恐跨、恐同以及各种其他形式的仇恨言论是困难的。信号可能因语言、文化、地理区域和特定在线平台等因素而有所不同。本文提出了一种结合多语言（M-L）与语言特定（L-S）的方法，用于恐同和恐跨仇恨言论检测（HSD）。需要 M-L 模型来捕捉在某种特定语言中较少见或缺失、并因此被 L-S 模型忽略的词语、短语和概念。尽管如此，L-S 模型更适合理解通常以某种特定语言写作的用户所处的文化和语言语境。本文通过简单的权重插值，构建了一种简单且成功的方式来融合 M-L 与 L-S 方法，使其兼具可解释性和数据驱动性。我们在“社交媒体评论中的恐同/恐跨检测共享任务”数据集的任务 A 上展示了该系统，用于恐同和恐跨 HSD。我们的系统在五种语言中的三种上取得了最佳结果，并在马拉雅拉姆语文本上取得了 0.997 的宏平均 F1 分数。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
