Enhancing Pun Meme Understanding with Visual Semantic Cues

Meng Li; Zhongqing Wang (王中卿); Guodong Zhou (周国栋)

Enhancing Pun Meme Understanding with Visual Semantic Cues

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Abstract

Internet memes have become important carriers of online expression, and pun memes, as a typical multimodal category, pose significant challenges to machine understanding due to their reliance on textual ambiguity and cross-modal interaction with visual content. Existing multimodal models for pun meme understanding suffer from classification bias, weak sentiment perception, and performance gaps compared to human comprehension, mainly due to neglecting fine-grained correlation cues for semantic reasoning. To address this, we propose a fusion framework enhanced by multi-source auxiliary cues, including visual salient perception, textual deep semantics, and abnormal pun linguistic features, which are embedded into the multimodal reasoning pipeline. Experiments show that our framework stably optimizes vision-language models, improving accuracy in pun meme detection and implicit sentiment fine-grained analysis.

Anthology ID:: 2026.findings-emnlp.1198
Volume:: Findings of the Association for Computational Linguistics: EMNLP 2026
Month:: October
Year:: 2026
Address:: Budapest, Hungary
Editors:: Hung-Yi Lee, Sunipa Dev, Jessy Li, Desmond Elliott
Venue:: Findings
SIG:: SIGDAT
Publisher:: Association for Computational Linguistics
Note:
Pages:: 25297–25308
Language:
URL:: https://aclanthology.org/2026.findings-emnlp.1198/
DOI:
Bibkey:
Cite (ACL):: Meng Li, Zhongqing Wang, and Guodong Zhou. 2026. Enhancing Pun Meme Understanding with Visual Semantic Cues. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 25297–25308, Budapest, Hungary. Association for Computational Linguistics.
Cite (Informal):: Enhancing Pun Meme Understanding with Visual Semantic Cues (Li et al., Findings 2026)
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PDF:: https://aclanthology.org/2026.findings-emnlp.1198.pdf

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@inproceedings{li-etal-2026-enhancing-pun,
    title = "Enhancing Pun Meme Understanding with Visual Semantic Cues",
    author = "Li, Meng  and
      Wang, Zhongqing  and
      Zhou, Guodong",
    editor = "Lee, Hung-Yi  and
      Dev, Sunipa  and
      Li, Jessy  and
      Elliott, Desmond",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
    month = oct,
    year = "2026",
    address = "Budapest, Hungary",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2026.findings-emnlp.1198/",
    pages = "25297--25308",
    ISBN = "979-8-89176-462-0",
    abstract = "Internet memes have become important carriers of online expression, and pun memes, as a typical multimodal category, pose significant challenges to machine understanding due to their reliance on textual ambiguity and cross-modal interaction with visual content. Existing multimodal models for pun meme understanding suffer from classification bias, weak sentiment perception, and performance gaps compared to human comprehension, mainly due to neglecting fine-grained correlation cues for semantic reasoning. To address this, we propose a fusion framework enhanced by multi-source auxiliary cues, including visual salient perception, textual deep semantics, and abnormal pun linguistic features, which are embedded into the multimodal reasoning pipeline. Experiments show that our framework stably optimizes vision-language models, improving accuracy in pun meme detection and implicit sentiment fine-grained analysis."
}

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%0 Conference Proceedings
%T Enhancing Pun Meme Understanding with Visual Semantic Cues
%A Li, Meng
%A Wang, Zhongqing
%A Zhou, Guodong
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F li-etal-2026-enhancing-pun
%X Internet memes have become important carriers of online expression, and pun memes, as a typical multimodal category, pose significant challenges to machine understanding due to their reliance on textual ambiguity and cross-modal interaction with visual content. Existing multimodal models for pun meme understanding suffer from classification bias, weak sentiment perception, and performance gaps compared to human comprehension, mainly due to neglecting fine-grained correlation cues for semantic reasoning. To address this, we propose a fusion framework enhanced by multi-source auxiliary cues, including visual salient perception, textual deep semantics, and abnormal pun linguistic features, which are embedded into the multimodal reasoning pipeline. Experiments show that our framework stably optimizes vision-language models, improving accuracy in pun meme detection and implicit sentiment fine-grained analysis.
%U https://aclanthology.org/2026.findings-emnlp.1198/
%P 25297-25308

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Markdown (Informal)

[Enhancing Pun Meme Understanding with Visual Semantic Cues](https://aclanthology.org/2026.findings-emnlp.1198/) (Li et al., Findings 2026)

Enhancing Pun Meme Understanding with Visual Semantic Cues (Li et al., Findings 2026)

ACL

Meng Li, Zhongqing Wang, and Guodong Zhou. 2026. Enhancing Pun Meme Understanding with Visual Semantic Cues. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 25297–25308, Budapest, Hungary. Association for Computational Linguistics.