TransMeme: A Multi-Agent Framework for Cross-Cultural Meme Transcreation

Jingyi Zheng; Yule Liu; Zifan Peng; Tianyi Hu; Yuemeng Zhao; Xinhu Zheng; Xinlei He

cs.AIAug 27, 2026

TransMeme: A Multi-Agent Framework for Cross-Cultural Meme Transcreation

Authors: Jingyi Zheng, Yule Liu, Zifan Peng, Tianyi Hu, Yuemeng Zhao, Xinhu Zheng, Xinlei He

Organizations: Hong Kong University of Science and Technology (Guangzhou) · Aarhus University · Wuhan University

Abstract

Internet memes are a pervasive form of multimodal online communication; however, such communication often involves users from diverse linguistic and cultural backgrounds. Therefore, adapting memes across cultures and languages is a central challenge for enabling mutual understanding in online communication. Unlike ordinary translation or standalone text rewriting, cross-cultural meme transcreation must jointly preserve communicative intent, adapt culture-dependent meaning for the target audience, and maintain coherence between text and image. In this work, we first provide an explicit task analysis of cross-cultural meme transcreation and identify three core challenges: culture-specific knowledge understanding, intent and tone preservation, and multimodal consistency. Based on this analysis, we propose a multi-agent framework with specialized agents that are coordinated to address these challenges through cultural adaptation, target text rewriting, revision, and conditional visual adjustment. The framework strengthens target text adaptation with coordinated feedback to handle difficult cases that require deeper cultural or visual intervention. We evaluate the framework on bidirectional Chinese-English meme transcreation using both human evaluation and LLM-as-a-Judge. Our method consistently outperforms all baselines across both evaluation settings. In human evaluation, it achieves the best performance on all four dimensions and delivers a 33.1% average improvement over the strongest baseline, while in LLM-as-a-Judge, it attains the highest Top-1 ranking rate (60% versus 26% for the second-best baseline). Further analysis indicates that each component contributes to the performance. Our error analysis suggests that the remaining bottlenecks lie in humor reconstruction and image-text alignment rather than simple cultural knowledge gaps, pointing to future work on humor transfer.

Figures & tables

Figure 1. Overview of our multi-agent framework for cross-cultural meme transcreation.

Task	XLang	Text	Visual	Culture	I-T
Machine Translation	✓	✓	✗	✗	✗
Multimodal Translation	✓	✓	✓	✗	✗
Text Rewriting	✗	✓	✗	✗	✗
Image Editing	✗	✗	✓	✗	✗
Meme Transcreation	✓	✓	✓	✓	✓

Table 1. Comparison between meme transcreation and related tasks. XLang : cross-lingual transfer; Text : textual adaptation; Visual : visual understanding or editing; Culture : cultural adaptation; I-T : image-text coherence.

Metric	All		ZH $\rightarrow$ EN		EN $\rightarrow$ ZH
Metric	Stat.	MAE	Stat.	MAE	Stat.	MAE
Intent ( $\rho$ )	0.802	0.617	0.811	0.592	0.787	0.642
Adaptation ( $\rho$ )	0.693	0.791	0.710	0.791	0.686	0.792
Coherence ( $\rho$ )	0.783	0.680	0.778	0.685	0.791	0.676
Quality ( $\rho$ )	0.789	0.748	0.807	0.669	0.765	0.827
Ranking ( $\tau$ )	0.422	-	0.419	-	0.425	-

Table 2. Alignment between human and LLM evaluation.

Figure 2. Main LLM-based results: (A) Dimension-wise comparison. (B) Average-score by transcreation direction. (C) Top-1 selection rate. Our method performs best overall, remains robust in both directions, and is most frequently ranked first.

Figure 3. Qualitative comparison of meme transcreation results. Our method produces target memes that better preserve humor, improve cultural appropriateness, and maintain image-text coherence.

Method	Intent	Adaptation	Coherence	Quality	Avg.
M	3.950	4.097	4.111	4.328	4.122
B1	1.187	1.328	1.180	1.172	1.217
B2	3.385	3.138	3.380	2.484	3.097
B3	1.716	1.818	1.910	1.800	1.811

Table 3. Aggregated human evaluation results, where our method performs best across all dimensions. Avg. denotes the mean of the four dimension scores.

Figure 4. Rewriter quality analysis: M consistently outperforms A1 and A2, confirming the benefit of DPO.

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