Caption Enriched Samples for Improving Hateful Memes Detection

• URL: http://arxiv.org/abs/2109.10649v1
• Date: Wed, 22 Sep 2021 10:57:51 GMT
• Title: Caption Enriched Samples for Improving Hateful Memes Detection
• Authors: Efrat Blaier, Itzik Malkiel, Lior Wolf
• Abstract summary: The hateful meme challenge demonstrates the difficulty of determining whether a meme is hateful or not. Both unimodal language models and multimodal vision-language models cannot reach the human level of performance.
• Score: 78.5136090997431
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The recently introduced hateful meme challenge demonstrates the difficulty of determining whether a meme is hateful or not. Specifically, both unimodal language models and multimodal vision-language models cannot reach the human level of performance. Motivated by the need to model the contrast between the image content and the overlayed text, we suggest applying an off-the-shelf image captioning tool in order to capture the first. We demonstrate that the incorporation of such automatic captions during fine-tuning improves the results for various unimodal and multimodal models. Moreover, in the unimodal case, continuing the pre-training of language models on augmented and original caption pairs, is highly beneficial to the classification accuracy.

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