Humor is a natural and fundamental component of human interactions. When correctly applied, humor allows us to express thoughts and feelings conveniently and effectively, increasing interpersonal affection, likeability, and trust. However, understanding the use of humor is a computationally challenging task from the perspective of humor-aware language processing models. As language models become ubiquitous through virtual-assistants and IOT devices, the need to develop humor-aware models rises exponentially. To further improve the state-of-the-art capacity to perform this particular sentiment-analysis task we must explore models that incorporate contextualized and nonverbal elements in their design. Ideally, we seek architectures accepting non-verbal elements as additional embedded inputs to the model, alongside the original sentence-embedded input. This survey thus analyses the current state of research in techniques for improved contextualized embedding incorporating nonverbal information, as well as newly proposed deep architectures to improve context retention on top of popular word-embeddings methods.

中文翻译：

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通过改进的情境化嵌入和模型体系结构推进幽默感分析

幽默是人类互动的自然和基本组成部分。如果正确使用幽默，幽默可以使我们方便有效地表达思想和感觉，从而增加人际关系，亲和力和信任感。但是，从了解幽默的语言处理模型的角度来看，了解幽默的使用是一项计算难题。随着语言模型通过虚拟助手和物联网设备的普及，开发幽默感知模型的需求呈指数增长。为了进一步提高执行此特定情感分析任务的最新能力，我们必须探索在设计中纳入上下文和非语言元素的模型。理想情况下，我们寻求接受非语言元素作为模型的附加嵌入式输入的体系结构，以及原始的句子嵌入输入。因此，本次调查分析了在结合非语言信息的情况下改进上下文嵌入的技术的研究现状，以及新近提出的深度体系结构，以在流行的词嵌入方法之上提高上下文保留率。