Align before Attend: Aligning Visual and Textual Features for Multimodal Hateful Content Detection

• URL: http://arxiv.org/abs/2402.09738v1
• Date: Thu, 15 Feb 2024 06:34:15 GMT
• Title: Align before Attend: Aligning Visual and Textual Features for Multimodal Hateful Content Detection
• Authors: Eftekhar Hossain, Omar Sharif, Mohammed Moshiul Hoque, Sarah M. Preum
• Abstract summary: This paper proposes a context-aware attention framework for multimodal hateful content detection. We evaluate the proposed approach on two benchmark hateful meme datasets, viz. MUTE (Bengali code-mixed) and MultiOFF (English)
• Score: 4.997673761305336
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Multimodal hateful content detection is a challenging task that requires complex reasoning across visual and textual modalities. Therefore, creating a meaningful multimodal representation that effectively captures the interplay between visual and textual features through intermediate fusion is critical. Conventional fusion techniques are unable to attend to the modality-specific features effectively. Moreover, most studies exclusively concentrated on English and overlooked other low-resource languages. This paper proposes a context-aware attention framework for multimodal hateful content detection and assesses it for both English and non-English languages. The proposed approach incorporates an attention layer to meaningfully align the visual and textual features. This alignment enables selective focus on modality-specific features before fusing them. We evaluate the proposed approach on two benchmark hateful meme datasets, viz. MUTE (Bengali code-mixed) and MultiOFF (English). Evaluation results demonstrate our proposed approach's effectiveness with F1-scores of $69.7$% and $70.3$% for the MUTE and MultiOFF datasets. The scores show approximately $2.5$% and $3.2$% performance improvement over the state-of-the-art systems on these datasets. Our implementation is available at https://github.com/eftekhar-hossain/Bengali-Hateful-Memes.

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