Sarcasm is a sophisticated way of wrapping any immanent truth, mes-sage, or even mockery within a hilarious manner. The advent of communications using social networks has mass-produced new avenues of socialization. It can be further said that humor, irony, sarcasm, and wit are the four chariots of being socially funny in the modern days. In this paper, we manually extract the sarcastic word distribution features of a benchmark pop culture sarcasm corpus, containing sarcastic dialogues and monologues. We generate input sequences formed of the weighted vectors from such words. We further propose an amalgamation of four parallel deep long-short term networks (pLSTM), each with distinctive activation classifier. These modules are primarily aimed at successfully detecting sarcasm from the text corpus. Our proposed model for detecting sarcasm peaks a training accuracy of 98.95% when trained with the discussed dataset. Consecutively, it obtains the highest of 98.31% overall validation accuracy on two handpicked Project Gutenberg English humor literature among all the test cases. Our approach transcends previous state-of-the-art works on several sarcasm corpora and results in a new gold standard performance for sarcasm detection.

中文翻译：

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从流行文化文本和英语幽默文学并行深度学习驱动的讽刺检测

讽刺是一种复杂的方式，可以用热闹的方式包装任何内在的真相、信息甚至嘲讽。使用社交网络进行通信的出现已经大量生产了新的社交途径。可以进一步说，幽默、讽刺、讽刺和机智是现代社会风趣的四辆马车。在本文中，我们手动提取了基准流行文化讽刺语料库的讽刺词分布特征，其中包含讽刺对话和独白。我们从这些词生成由加权向量组成的输入序列。我们进一步提出了四个并行深度长短期网络 (pLSTM) 的合并，每个网络都具有独特的激活分类器。这些模块主要旨在成功地从文本语料库中检测出讽刺。我们提出的用于检测讽刺的模型在使用所讨论的数据集进行训练时达到了 98.95% 的训练准确率。连续地，它在所有测试用例中对两个精心挑选的 Project Gutenberg 英语幽默文学作品获得了 98.31% 的最高总体验证准确率。我们的方法超越了之前在几个讽刺语料库上的最先进工作，并为讽刺检测提供了新的黄金标准性能。