锻炼？我以为你说的是“额外的薯条”：利用句子划分和多跳注意力进行Meme情感分析
Exercise? I thought you said 'Extra Fries': Leveraging Sentence Demarcations and Multi-hop Attention for Meme Affect Analysis
摘要：
今天的互联网上充斥着模因，因为它们是幽默的，讽刺的，或讽刺的，让人发笑。根据一项调查，33%的社交媒体用户在年龄段[13-35]每天发送模因，而超过50%的人每周发送。其中一些模因在很短的时间内迅速传播，它们的病毒式传播依赖于其（文本和视觉）内容的新颖性。其中一些传达积极的信息，如有趣或激励性的报价;而其他人则是为了通过讽刺或攻击性的信息来嘲笑/伤害某人的感情。尽管模因的吸引力和他们在社交媒体上的迅速出现，模因的有效分析还没有得到充分的尝试，它应得的程度。在本文中，我们试图解决SemEval'20-Memotion分析比赛中提出的同一组任务。我们提出了一个基于多跳注意力的深度神经网络框架，称为MHA-MEME，其主要目标是利用视觉模态（图像）和各种文本片段之间的空间域对应关系来提取细粒度的特征表示进行分类。我们在“Memotion Analysis”数据集上评估了MHA-MEME的所有三个子任务-情感分类，情感分类和情感类量化。我们的比较研究显示，与参加比赛的顶级系统相比，MHA-MEME在所有三项任务中的表现都很好。与所有三个任务之间表现不一致的基线不同，MHA-MEME在所有任务中的平均表现都优于基线。此外，我们在另一组手动注释的测试样本上验证了MHA-MEME的泛化，并观察到它是一致的。最后，我们建立了MHA-MEME的可解释性。
英文摘要：
Today's Internet is awash in memes as they are humorous, satirical, or ironic which make people laugh. According to a survey, 33% of social media users in age bracket [13-35] send memes every day, whereas more than 50% send every week. Some of these memes spread rapidly within a very short time-frame, and their virality depends on the novelty of their (textual and visual) content. A few of them convey positive messages, such as funny or motivational quotes; while others are meant to mock/hurt someone's feelings through sarcastic or offensive messages. Despite the appealing nature of memes and their rapid emergence on social media, effective analysis of memes has not been adequately attempted to the extent it deserves. In this paper, we attempt to solve the same set of tasks suggested in the SemEval'20-Memotion Analysis competition. We propose a multi-hop attention-based deep neural network framework, called MHA-MEME, whose prime objective is to leverage the spatial-domain correspondence between the visual modality (an image) and various textual segments to extract fine-grained feature representations for classification. We evaluate MHA-MEME on the 'Memotion Analysis' dataset for all three sub-tasks - sentiment classification, affect classification, and affect class quantification. Our comparative study shows sota performances of MHA-MEME for all three tasks compared to the top systems that participated in the competition. Unlike all the baselines which perform inconsistently across all three tasks, MHA-MEME outperforms baselines in all the tasks on average. Moreover, we validate the generalization of MHA-MEME on another set of manually annotated test samples and observe it to be consistent. Finally, we establish the interpretability of MHA-MEME.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
