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pro vyhledávání: '"Zhang, Yigeng"'
We address the challenge of detecting questionable content in online media, specifically the subcategory of comic mischief. This type of content combines elements such as violence, adult content, or sarcasm with humor, making it difficult to detect.
Externí odkaz:
http://arxiv.org/abs/2406.07841
Reading comprehension continues to be a crucial research focus in the NLP community. Recent advances in Machine Reading Comprehension (MRC) have mostly centered on literal comprehension, referring to the surface-level understanding of content. In thi
Externí odkaz:
http://arxiv.org/abs/2404.05250
In this work, we introduce a pioneering research challenge: evaluating positive and potentially harmful messages within music products. We initiate by setting a multi-faceted, multi-task benchmark for music content assessment. Subsequently, we introd
Externí odkaz:
http://arxiv.org/abs/2309.10182
In the field of cross-modal retrieval, single encoder models tend to perform better than dual encoder models, but they suffer from high latency and low throughput. In this paper, we present a dual encoder model called BagFormer that utilizes a cross
Externí odkaz:
http://arxiv.org/abs/2212.14322
Publikováno v:
In Engineering Applications of Artificial Intelligence October 2024 136 Part B
In this paper, we introduce the task of predicting severity of age-restricted aspects of movie content based solely on the dialogue script. We first investigate categorizing the ordinal severity of movies on 5 aspects: Sex, Violence, Profanity, Subst
Externí odkaz:
http://arxiv.org/abs/2109.09276
Satirical news is regularly shared in modern social media because it is entertaining with smartly embedded humor. However, it can be harmful to society because it can sometimes be mistaken as factual news, due to its deceptive character. We found tha
Externí odkaz:
http://arxiv.org/abs/2007.02164
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