论文标题
rubcsg在Semeval-2022任务5：识别厌恶模因的合奏学习
RubCSG at SemEval-2022 Task 5: Ensemble learning for identifying misogynous MEMEs
论文作者
论文摘要
这项工作介绍了一个基于为Semeval 2022任务开发的各种单模式和双模式模型架构5：Mami-Multimedia自动厌食症识别。挑战组织者提供了一个英语模因数据集来开发和培训系统，以识别和分类厌恶的模因。更确切地说，竞争分为两个子任务：子任务A要求关于模因是否表达厌女症的二进制决定，而子任务B则是将厌恶的模因分类为可能重叠的刻板印象，羞辱，客观化和暴力的潜在重叠的子类别。为了提交，我们实施了一个新的模型融合网络，并采用合奏学习方法来提高性能。通过这种结构，我们在子任务A中获得了0.755宏观的F1得分（11th），并在子任务B中获得0.709的加权平均得分（第10）。
This work presents an ensemble system based on various uni-modal and bi-modal model architectures developed for the SemEval 2022 Task 5: MAMI-Multimedia Automatic Misogyny Identification. The challenge organizers provide an English meme dataset to develop and train systems for identifying and classifying misogynous memes. More precisely, the competition is separated into two sub-tasks: sub-task A asks for a binary decision as to whether a meme expresses misogyny, while sub-task B is to classify misogynous memes into the potentially overlapping sub-categories of stereotype, shaming, objectification, and violence. For our submission, we implement a new model fusion network and employ an ensemble learning approach for better performance. With this structure, we achieve a 0.755 macroaverage F1-score (11th) in sub-task A and a 0.709 weighted-average F1-score (10th) in sub-task B.
