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        <title>MemeCap: A Dataset for Captioning and Interpreting Memes</title>
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            <title>Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing</title>
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    <abstract>Memes are a widely popular tool for web users to express their thoughts using visual metaphors. Understanding memes requires recognizing and interpreting visual metaphors with respect to the text inside or around the meme, often while employing background knowledge and reasoning abilities. We present the task of meme captioning and release a new dataset, MemeCap. Our dataset contains 6.3K memes along with the title of the post containing the meme, the meme captions, the literal image caption, and the visual metaphors. Despite the recent success of vision and language (VL) models on tasks such as image captioning and visual question answering, our extensive experiments using state-of-the-art VL models show that they still struggle with visual metaphors, and perform substantially worse than humans.</abstract>
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    <identifier type="doi">10.18653/v1/2023.emnlp-main.89</identifier>
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%0 Conference Proceedings
%T MemeCap: A Dataset for Captioning and Interpreting Memes
%A Hwang, EunJeong
%A Shwartz, Vered
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F hwang-shwartz-2023-memecap
%X Memes are a widely popular tool for web users to express their thoughts using visual metaphors. Understanding memes requires recognizing and interpreting visual metaphors with respect to the text inside or around the meme, often while employing background knowledge and reasoning abilities. We present the task of meme captioning and release a new dataset, MemeCap. Our dataset contains 6.3K memes along with the title of the post containing the meme, the meme captions, the literal image caption, and the visual metaphors. Despite the recent success of vision and language (VL) models on tasks such as image captioning and visual question answering, our extensive experiments using state-of-the-art VL models show that they still struggle with visual metaphors, and perform substantially worse than humans.
%R 10.18653/v1/2023.emnlp-main.89
%U https://aclanthology.org/2023.emnlp-main.89/
%U https://doi.org/10.18653/v1/2023.emnlp-main.89
%P 1433-1445

Markdown (Informal)

[MemeCap: A Dataset for Captioning and Interpreting Memes](https://aclanthology.org/2023.emnlp-main.89/) (Hwang & Shwartz, EMNLP 2023)

• MemeCap: A Dataset for Captioning and Interpreting Memes (Hwang & Shwartz, EMNLP 2023)

ACL

• EunJeong Hwang and Vered Shwartz. 2023. MemeCap: A Dataset for Captioning and Interpreting Memes. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 1433–1445, Singapore. Association for Computational Linguistics.