论文标题
Quarc：Quataternion多模式融合体系结构仇恨言语分类
QUARC: Quaternion Multi-Modal Fusion Architecture For Hate Speech Classification
论文作者
论文摘要
仇恨言论在社交媒体时代很普遍，有时是无害的，但也可能给社区中的某人甚至骚乱造成精神创伤。宗教符号的图像带有贬义的评论或虐待特定社区的男人的视频，所有人都以其每种方式（例如文本，图像和音频）的仇恨言论。基于社交媒体上仇恨言论的特定模式的模型无用，而是，我们需要在对仇恨言论进行分类时同时考虑图像和文本的多模式融合模型之类的模型。文本图像融合模型被大量参数化，因此我们提出了一个基于四个神经网络的模型，每对模态具有附加的融合组件。该模型在MMHS150K Twitter数据集上进行了测试，以进行仇恨语音分类。该模型显示，参数降低了几乎75％，并且在存储空间和培训时间方面也使我们受益，而与其真正的对应物相比，在性能方面处于标准方面。
Hate speech, quite common in the age of social media, at times harmless but can also cause mental trauma to someone or even riots in communities. Image of a religious symbol with derogatory comment or video of a man abusing a particular community, all become hate speech with its every modality (such as text, image, and audio) contributing towards it. Models based on a particular modality of hate speech post on social media are not useful, rather, we need models like multi-modal fusion models that consider both image and text while classifying hate speech. Text-image fusion models are heavily parameterized, hence we propose a quaternion neural network-based model having additional fusion components for each pair of modalities. The model is tested on the MMHS150K twitter dataset for hate speech classification. The model shows an almost 75% reduction in parameters and also benefits us in terms of storage space and training time while being at par in terms of performance as compared to its real counterpart.
