解读仇恨：识别仇恨模因及其目标
Deciphering Hate: Identifying Hateful Memes and Their Targets
AI总结：
针对孟加拉语低资源仇恨模因检测及其目标实体识别的研究空白，提出了多模态数据集 BHM 和双协同注意力框架 DORA，通过联合评估模态特征与模态特定特征，在两项任务上优于现有基线并具备良好泛化能力。
摘要：
互联网模因已成为个人在社交媒体上表达情感、思想和观点的有力工具。虽然模因常被视为幽默和娱乐的来源，但它们也可能传播针对个人或社区的仇恨内容。大多数现有研究集中于高资源语言中模因的负面层面，忽视了与孟加拉语等低资源语言相关的独特挑战。此外，虽然之前关于孟加拉语模因的工作集中在检测仇恨模因上，但尚未有工作检测其目标实体。为了填补这一空白并促进该领域的研究，我们为孟加拉语引入了一个新颖的多模态数据集 BHM（Bengali Hateful Memes）。该数据集包含 7,148 个带有孟加拉语及混合代码标题的模因，专为两个任务量身定制：（i）检测仇恨模因，以及（ii）检测它们所针对的社会实体（即个人、组织、社区和社会）。为了解决这些任务，我们提出了 DORA（Dual cO attention fRAmework），这是一个多模态深度神经网络，系统地从模因中提取显著的模态特征，并与模态特定特征联合评估它们，以更好地理解上下文。我们的实验表明，DORA 在其他低资源仇恨模因数据集上具有泛化能力，并优于几个最先进的竞争基线。
英文摘要：
Internet memes have become a powerful means for individuals to express emotions, thoughts, and perspectives on social media. While often considered as a source of humor and entertainment, memes can also disseminate hateful content targeting individuals or communities. Most existing research focuses on the negative aspects of memes in high-resource languages, overlooking the distinctive challenges associated with low-resource languages like Bengali (also known as Bangla). Furthermore, while previous work on Bengali memes has focused on detecting hateful memes, there has been no work on detecting their targeted entities. To bridge this gap and facilitate research in this arena, we introduce a novel multimodal dataset for Bengali, BHM (Bengali Hateful Memes). The dataset consists of 7,148 memes with Bengali as well as code-mixed captions, tailored for two tasks: (i) detecting hateful memes, and (ii) detecting the social entities they target (i.e., Individual, Organization, Community, and Society). To solve these tasks, we propose DORA (Dual cO attention fRAmework), a multimodal deep neural network that systematically extracts the significant modality features from the memes and jointly evaluates them with the modality-specific features to understand the context better. Our experiments show that DORA is generalizable on other low-resource hateful meme datasets and outperforms several state-of-the-art rivaling baselines.
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。
