@inproceedings{jiang-etal-2026-joke,
title = "Why Is This Joke Offensive? Interpreting Offensiveness in {C}hinese Dark Humor Through Knowledge and Rhetoric",
author = "Jiang, Jiecheng and
Wan, Herun and
Fu, Zihang and
Yu, Hai-Tao and
Kan, Min-Yen and
Wu, Jiaying",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.204/",
pages = "4675--4696",
ISBN = "979-8-89176-459-0",
abstract = "Offensive dark humor presents a high-context safety challenge: its harmful meaning is often absent from surface wording and emerges only after recovering background knowledge and rhetorical transformation. To explore this problem, we introduce HellHumor-CN, a benchmark of 2,168 Chinese jokes annotated for three subtasks: (1) offense detection, (2) sensitive topic prediction, and (3) target identification. We further propose HumorLens, a knowledge-grounded and rhetoric-aware multi-agent framework that retrieves relevant background information and analyzes three common mechanisms in dark humor: mockery, irony, and pun. Experiments show that toxic content detectors and strong LLMs struggle with Chinese dark humor, especially on topic and target interpretation. HumorLens consistently improves performance across multiple models, suggesting that background grounding and mechanism-specific analysis help identify implicit offensive meanings. Further analyses reveal persistent challenges, including visually implied harm, coded sexual innuendo, lexical oversensitivity, and abstract target grounding."
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<abstract>Offensive dark humor presents a high-context safety challenge: its harmful meaning is often absent from surface wording and emerges only after recovering background knowledge and rhetorical transformation. To explore this problem, we introduce HellHumor-CN, a benchmark of 2,168 Chinese jokes annotated for three subtasks: (1) offense detection, (2) sensitive topic prediction, and (3) target identification. We further propose HumorLens, a knowledge-grounded and rhetoric-aware multi-agent framework that retrieves relevant background information and analyzes three common mechanisms in dark humor: mockery, irony, and pun. Experiments show that toxic content detectors and strong LLMs struggle with Chinese dark humor, especially on topic and target interpretation. HumorLens consistently improves performance across multiple models, suggesting that background grounding and mechanism-specific analysis help identify implicit offensive meanings. Further analyses reveal persistent challenges, including visually implied harm, coded sexual innuendo, lexical oversensitivity, and abstract target grounding.</abstract>
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%0 Conference Proceedings
%T Why Is This Joke Offensive? Interpreting Offensiveness in Chinese Dark Humor Through Knowledge and Rhetoric
%A Jiang, Jiecheng
%A Wan, Herun
%A Fu, Zihang
%A Yu, Hai-Tao
%A Kan, Min-Yen
%A Wu, Jiaying
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F jiang-etal-2026-joke
%X Offensive dark humor presents a high-context safety challenge: its harmful meaning is often absent from surface wording and emerges only after recovering background knowledge and rhetorical transformation. To explore this problem, we introduce HellHumor-CN, a benchmark of 2,168 Chinese jokes annotated for three subtasks: (1) offense detection, (2) sensitive topic prediction, and (3) target identification. We further propose HumorLens, a knowledge-grounded and rhetoric-aware multi-agent framework that retrieves relevant background information and analyzes three common mechanisms in dark humor: mockery, irony, and pun. Experiments show that toxic content detectors and strong LLMs struggle with Chinese dark humor, especially on topic and target interpretation. HumorLens consistently improves performance across multiple models, suggesting that background grounding and mechanism-specific analysis help identify implicit offensive meanings. Further analyses reveal persistent challenges, including visually implied harm, coded sexual innuendo, lexical oversensitivity, and abstract target grounding.
%U https://aclanthology.org/2026.emnlp-main.204/
%P 4675-4696
Markdown (Informal)
[Why Is This Joke Offensive? Interpreting Offensiveness in Chinese Dark Humor Through Knowledge and Rhetoric](https://aclanthology.org/2026.emnlp-main.204/) (Jiang et al., EMNLP 2026)
ACL