Many online comments on social media platforms are hateful, humorous, or sarcastic. The sarcastic nature of these comments (especially the short ones) alters their actual implied sentiments, which leads to misinterpretations by the existing sentiment analysis models. A lot of research has already been done to detect sarcasm in the text using user-based, topical, and conversational information but not much work has been done to use inter-sentence contextual information for detecting the same. This paper proposes a new state-of-the-art deep learning architecture that uses a novel Bidirectional Inter-Sentence Contextual Attention mechanism (Bi-ISCA) to capture inter-sentence dependencies for detecting sarcasm in the user-generated short text using only the conversational context. The proposed deep learning model demonstrates the capability to capture explicit, implicit, and contextual incongruous words & phrases responsible for invoking sarcasm. Bi-ISCA generates state-of-the-art results on two widely used benchmark datasets for the sarcasm detection task (Reddit and Twitter). To the best of our knowledge, none of the existing state-of-the-art models use an inter-sentence contextual attention mechanism to detect sarcasm in the user-generated short text using only conversational context.

中文翻译：

---

Bi-ISCA：用于检测用户生成的嘈杂短文本中的讽刺的双向句子间上下文注意机制

社交媒体平台上的许多在线评论都是令人讨厌，幽默或讽刺的。这些评论（尤其是简短评论）的讽刺性质改变了它们的实际隐含情感，这导致了现有情感分析模型的误解。已经进行了很多研究来使用基于用户的，主题和对话信息来检测文本中的讽刺，但是还没有做很多工作来使用句子间上下文信息来检测它们。本文提出了一种最新的深度学习体系结构，该体系结构使用一种新颖的双向句子间上下文注意机制（Bi-ISCA）来捕获句子间依赖性，从而仅使用用户自己生成的短文本来检测讽刺。对话环境。拟议的深度学习模型演示了捕获引起讽刺的显式，隐式和上下文不协调单词和短语的能力。Bi-ISCA在两个广泛使用的基准数据集上产生了最新的结果，用于讽刺检测任务（Reddit和Twitter）。据我们所知，现有的最新模型都没有使用句子间上下文关注机制来仅通过会话上下文来检测用户生成的短文本中的讽刺。