论文标题
通过改进的情境化嵌入和模型体系结构来推进以幽默为中心的情感分析
Advancing Humor-Focused Sentiment Analysis through Improved Contextualized Embeddings and Model Architecture
论文作者
论文摘要
幽默是人类互动的自然和基本组成部分。当正确应用时，幽默使我们能够方便有效地表达思想和感受，增强人际情感，可爱性和信任。但是，从幽默意识语言处理模型的角度来看，了解幽默的使用是一项计算挑战的任务。随着语言模型通过虚拟辅助和物联网设备无处不在，开发幽默感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，我们必须探索将上下文化和非语言元素纳入其设计中的模型。理想情况下，我们寻求接受非语言元素作为模型的其他嵌入式输入的体系结构，以及原始句子的输入。因此，这项调查分析了改进融合非语言信息的上下文化嵌入技术中的研究状态，以及新提出的深层体系结构，以改善流行单词插入方法的上下文保留。
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.
