MHS-STMA：基于可扩展Transformer的多级注意力框架的多模态仇恨言论检测
MHS-STMA: Multimodal Hate Speech Detection via Scalable Transformer-Based Multilevel Attention Framework
AI总结：
针对现有仇恨言论检测多依赖单模态分析、多模态分析易忽略模态独特特征的问题，提出基于Transformer的多级注意力可扩展架构MHS-STMA，经三个数据集验证性能优于基线方法。
摘要：
社交媒体对人们的生活有着重要影响。近年来，社交媒体上的仇恨言论已成为社会最严重的问题之一。文本和图片是文章中传播的两种多模态数据形式。早期的方法主要聚焦于单模态分析。此外，研究人员在进行多模态分析时，往往忽略了保留每种模态的独特特征。为解决这些不足，本文提出了一种用于多模态仇恨内容检测的可扩展架构，即基于Transformer的多级注意力（STMA）。该架构包含三个主要部分：基于组合注意力的深度学习机制、视觉注意力机制编码器，以及字幕注意力机制编码器。每个组件都采用不同的注意力流程，以独特的方式处理多模态数据，从而识别仇恨内容。多项研究在Hateful memes、MultiOff和MMHS150K三个仇恨言论数据集上采用多种评估标准，验证了所提架构的有效性。结果表明，在全部三个数据集上，所提方法的性能均优于基线方法。
英文摘要：
Social media has a significant impact on people's lives. Hate speech on social media has emerged as one of society's most serious issues in recent years. Text and pictures are two forms of multimodal data that are distributed within articles. Unimodal analysis has been the primary emphasis of earlier approaches. Additionally, when doing multimodal analysis, researchers neglect to preserve the distinctive qualities associated with each modality. To address these shortcomings, the present article suggests a scalable architecture for multimodal hate content detection called transformer-based multilevel attention (STMA). This architecture consists of three main parts: a combined attention-based deep learning mechanism, a vision attention-mechanism encoder, and a caption attention-mechanism encoder. To identify hate content, each component uses various attention processes and handles multimodal data in a unique way. Several studies employing multiple assessment criteria on three hate speech datasets such as Hateful memes, MultiOff, and MMHS150K, validate the suggested architecture's efficacy. The outcomes demonstrate that on all three datasets, the suggested strategy performs better than the baseline approaches.
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