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arXiv · 2012.08312

QUARC: Quaternion Multi-Modal Fusion Architecture For Hate Speech Classification

Deepak Kumar·Nalin Kumar·Subhankar Mishra
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Abstract

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.

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Deepak Kumar, Nalin Kumar, Subhankar Mishra. 2020-12-15. QUARC: Quaternion Multi-Modal Fusion Architecture For Hate Speech Classification. https://arxiv.org/abs/2012.08312

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