MasonPerplexity在2024多模态仇恨言论事件检测中的表现：使用Transformer集成进行仇恨言论与目标检测
MasonPerplexity at Multimodal Hate Speech Event Detection 2024: Hate Speech and Target Detection Using Transformer Ensembles
AI总结：
本文提出MasonPerplexity系统，使用XLM-roBERTa-large及多模型集成方法处理多模态仇恨言论及目标检测任务，在CASE 2024两项子任务中均取得第3名。
摘要：
自动识别仇恨言论等冒犯性语言对于维持在线社区讨论的文明至关重要。在多模态内容中识别仇恨言论是一项极具挑战性的任务，因为冒犯性可能体现在文字、图像或两者的并置中。本文介绍了提交至EACL 2024的CASE 2024多模态仇恨言论事件检测共享任务的MasonPerplexity系统。该任务分为两个子任务：子任务A侧重于识别仇恨言论，子任务B侧重于识别政治事件期间文本嵌入图像中的目标。我们使用XLM-roBERTa-large模型处理子任务A，并使用结合XLM-roBERTa-base、BERTweet-large和BERT-base的集成方法处理子任务B。我们的方法在子任务A中取得0.8347的F1分数，在子任务B中取得0.6741的F1分数，在两项子任务中均排名第3。
英文摘要：
The automatic identification of offensive language such as hate speech is important to keep discussions civil in online communities. Identifying hate speech in multimodal content is a particularly challenging task because offensiveness can be manifested in either words or images or a juxtaposition of the two. This paper presents the MasonPerplexity submission for the Shared Task on Multimodal Hate Speech Event Detection at CASE 2024 at EACL 2024. The task is divided into two sub-tasks: sub-task A focuses on the identification of hate speech and sub-task B focuses on the identification of targets in text-embedded images during political events. We use an XLM-roBERTa-large model for sub-task A and an ensemble approach combining XLM-roBERTa-base, BERTweet-large, and BERT-base for sub-task B. Our approach obtained 0.8347 F1-score in sub-task A and 0.6741 F1-score in sub-task B ranking 3rd on both sub-tasks.
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。
