SMILE-Next: Teaching Large Language Models to Detect, Classify, and Reason about Laughter (2026.acl-long)
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| Challenge: | Existing approaches to understanding laughter or humor focus on narrowly defined tasks such as detecting humor and estimating humor intensity. |
| Approach: | They propose a dataset for real-world laughter understanding with multimodal textual representations and question–answer annotations. |
| Outcome: | The proposed framework outperforms baselines in three laughter-related tasks, showing that it is robust. |
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| Challenge: | Despite advances in artificial intelligence, building social intelligence remains a challenge. |
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| Challenge: | Using TED talks, we use laughter detection software to capture humor in the sitcom genre. |
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| Challenge: | a new multimodal dataset of stand-up comedies is proposed to improve humor detection . the dataset is the biggest available for this type of task, and the most diverse . |
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Md Kamrul Hasan, Wasifur Rahman, AmirAli Bagher Zadeh, Jianyuan Zhong, Md Iftekhar Tanveer, Louis-Philippe Morency, Mohammed (Ehsan) Hoque
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Zhengpeng Shi, Yanpeng Zhao, Jianqun Zhou, Yuxuan Wang, Qinrong Cui, Wei Bi, Song-Chun Zhu, Bo Zhao, Zilong Zheng
| Challenge: | Humor enriches our daily lives and appears in many forms, from jokes and cartoons to comedies and viral videos. |
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| Challenge: | Large language models (LLMs) have shown strong performance in NLP tasks like text summarization and question answering. |
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Multimodal large language models for inclusive collaboration learning tasks (2022.naacl-srw)
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| Challenge: | This project leverages advances in multimodal large language models to build an inclusive collaboration feedback loop for participants developing general collaboration skills. |
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