In this paper, we describe our approaches and results for Task 2 of the LT-EDI 2024 Workshop, aimed at detecting homophobia and/or transphobia across ten languages. Our methodologies include monolingual transformers and ensemble methods, capitalizing on the strengths of each to enhance the performance of the models. The ensemble models worked well, placing our team, MasonTigers, in the top five for eight of the ten languages, as measured by the macro F1 score. Our work emphasizes the efficacy of ensemble methods in multilingual scenarios, addressing the complexities of language-specific tasks.

翻译：本文描述了我们在LT-EDI 2024研讨会任务2中的方法与结果，该任务旨在检测十种语言中的恐同和/或恐跨性别言论。我们的方法包括单语Transformer模型与集成方法，充分利用各自优势以提升模型性能。集成模型表现良好，使我们的团队MasonTigers在十种语言中的八种语言上跻身前五名（基于宏F1分数衡量）。本研究强调了集成方法在多语言场景中的有效性，并应对了语言特定任务的复杂性。