arXiv:2509. 25773v3 Announce Type: replace-cross Abstract: AI models capable of comprehending humor hold real-world promise -- for example, enhancing engagement in human-machine interactions.
By Zhengpeng Shi, Yanpeng Zhao, Jianqun Zhou, Yuxuan Wang, Qinrong Cui, Wei Bi, Songchun Zhu, Bo Zhao, Zilong Zheng
arXiv:2607. 15442v1 Announce Type: new Abstract: Internet memes intertwine visual cues, textual content, and cultural context, making them particularly challenging to interpret in scenarios where humor, sarcasm, and harmful intent coexist.
By Shanhong Liu, Pai Chet Ng, De Wen Soh, Malika Meghjani, Konstantinos N. Plataniotis
arXiv:2606. 00046v1 Announce Type: cross Abstract: Video platforms such as YouTube have reshaped how users engage with entertainment and information, emphasizing brief, highly engaging content such as Shorts.
By Sydney Johns, Sanjeev Parthasarathy, Shantnu Bhalla, Vaibhav Garg
arXiv:2606. 00022v1 Announce Type: cross Abstract: Humor generation remains difficult not only because producing fluent, novel jokes is hard, but because "funny" is audience-dependent and supervision is noisy -- preferences vary with audience, context, and culture, and annotator agreement is often low.
By Alexey Tikhonov, Alexey Ivanov
arXiv:2507. 06506v2 Announce Type: replace-cross Abstract: Translating wordplay across languages presents unique challenges that have long confounded both professional human translators and machine translation systems.
By Russell Taylor, Benjamin Herbert, Michael Sana
Social media videos often communicate meanings that go beyond their visible actions, captions, or speech. A mundane clip may become humorous, ironic, or satire only through the interaction of multimodal cues and cultural context, making such content a difficult test case for video-language models.