Beyond Translation: Cross-Cultural Meme Transcreation with Vision-Language Models

ACL ARR 2026 January Submission1046 Authors

Keywords: cross-cultural meme, transcreation, multimodal generation, vision–language, humor preservation, cultural adaptation, directional asymmetry, Chinese–US memes, dataset

Abstract: Memes are a pervasive form of online communication, yet their cultural specificity poses significant challenges for cross-cultural adaptation. We study cross-cultural meme transcreation, a multimodal generation task that aims to preserve communicative intent and humor while adapting culture-specific references. We propose a hybrid transcreation framework based on vision–language models and introduce a large-scale bidirectional dataset of Chinese and US memes. Using both human judgments and automated evaluation, we analyze 6,315 meme pairs and assess transcreation quality across cultural directions. Our results show that current vision–language models can perform cross-cultural meme transcreation to a limited extent, but exhibit clear directional asymmetries: US -> Chinese transcreation consistently achieves higher quality than Chinese -> US. We further identify which aspects of humor and visual–textual design transfer across cultures and which remain challenging, and propose an evaluation framework for assessing cross-cultural multimodal generation. Our code and dataset are publicly available at https://anonymous.4open.science/r/MemeXGen/.

Paper Type: Long

Research Area: Multimodality and Language Grounding to Vision, Robotics and Beyond

Research Area Keywords: vision language navigation,image text matching; cross-modal content generation,multimodality

Contribution Types: Model analysis & interpretability, NLP engineering experiment, Publicly available software and/or pre-trained models, Data resources, Data analysis

Languages Studied: english, chinese

Submission Number: 1046