@inproceedings{bates-etal-2025-template,
title = "A Template Is All You Meme",
author = "Bates, Luke and
Christensen, Peter Ebert and
Nakov, Preslav and
Gurevych, Iryna",
editor = "Chiruzzo, Luis and
Ritter, Alan and
Wang, Lu",
booktitle = "Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)",
month = apr,
year = "2025",
address = "Albuquerque, New Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.naacl-long.525/",
doi = "10.18653/v1/2025.naacl-long.525",
pages = "10443--10475",
ISBN = "979-8-89176-189-6",
abstract = "Templatic memes, characterized by a semantic structure adaptable to the creator{'}s intent, represent a significant yet underexplored area within meme processing literature. With the goal of establishing a new direction for computational meme analysis, here we create a knowledge base composed of more than 5,200 meme templates, information about them, and 54,000 examples of template instances (templatic memes). To investigate the semantic signal of meme templates, we show that we can match memes in datasets to base templates contained in our knowledge base with a distance-based lookup. To demonstrate the power of meme templates, we create TSplit, a method to reorganize datasets, where a template or templatic instance can only appear in either the training or test split. Our re-split datasets enhance general meme knowledge and improve sample efficiency, leading to more robust models. Our examination of meme templates results in state-of-the-art performance for every dataset we consider, paving the way for analysis grounded in templateness."
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<abstract>Templatic memes, characterized by a semantic structure adaptable to the creator’s intent, represent a significant yet underexplored area within meme processing literature. With the goal of establishing a new direction for computational meme analysis, here we create a knowledge base composed of more than 5,200 meme templates, information about them, and 54,000 examples of template instances (templatic memes). To investigate the semantic signal of meme templates, we show that we can match memes in datasets to base templates contained in our knowledge base with a distance-based lookup. To demonstrate the power of meme templates, we create TSplit, a method to reorganize datasets, where a template or templatic instance can only appear in either the training or test split. Our re-split datasets enhance general meme knowledge and improve sample efficiency, leading to more robust models. Our examination of meme templates results in state-of-the-art performance for every dataset we consider, paving the way for analysis grounded in templateness.</abstract>
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%0 Conference Proceedings
%T A Template Is All You Meme
%A Bates, Luke
%A Christensen, Peter Ebert
%A Nakov, Preslav
%A Gurevych, Iryna
%Y Chiruzzo, Luis
%Y Ritter, Alan
%Y Wang, Lu
%S Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)
%D 2025
%8 April
%I Association for Computational Linguistics
%C Albuquerque, New Mexico
%@ 979-8-89176-189-6
%F bates-etal-2025-template
%X Templatic memes, characterized by a semantic structure adaptable to the creator’s intent, represent a significant yet underexplored area within meme processing literature. With the goal of establishing a new direction for computational meme analysis, here we create a knowledge base composed of more than 5,200 meme templates, information about them, and 54,000 examples of template instances (templatic memes). To investigate the semantic signal of meme templates, we show that we can match memes in datasets to base templates contained in our knowledge base with a distance-based lookup. To demonstrate the power of meme templates, we create TSplit, a method to reorganize datasets, where a template or templatic instance can only appear in either the training or test split. Our re-split datasets enhance general meme knowledge and improve sample efficiency, leading to more robust models. Our examination of meme templates results in state-of-the-art performance for every dataset we consider, paving the way for analysis grounded in templateness.
%R 10.18653/v1/2025.naacl-long.525
%U https://aclanthology.org/2025.naacl-long.525/
%U https://doi.org/10.18653/v1/2025.naacl-long.525
%P 10443-10475
Markdown (Informal)
[A Template Is All You Meme](https://aclanthology.org/2025.naacl-long.525/) (Bates et al., NAACL 2025)
ACL
- Luke Bates, Peter Ebert Christensen, Preslav Nakov, and Iryna Gurevych. 2025. A Template Is All You Meme. In Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), pages 10443–10475, Albuquerque, New Mexico. Association for Computational Linguistics.