论文标题
BI-ISCA：双向句子间句子上下文注意机制，用于检测用户生成的噪声短文中的讽刺
Bi-ISCA: Bidirectional Inter-Sentence Contextual Attention Mechanism for Detecting Sarcasm in User Generated Noisy Short Text
论文作者
论文摘要
社交媒体平台上的许多在线评论是可恶，幽默或讽刺的。这些评论的讽刺性（尤其是简短的评论）改变了它们的实际隐含情感，这导致了现有的情感分析模型的误解。已经进行了大量研究来使用基于用户的，局部和对话信息来检测文本中的讽刺，但是使用句子间的上下文信息来检测该信息并没有做太多工作。本文提出了一种新的最先进的深度学习体系结构，该体系结构使用一种新型的双向句子间言语上下文注意机制（BI-ISCA），以捕获仅使用对话性上下文的用户生成的短文中检测讽刺的句子间依赖性。提出的深度学习模型展示了捕获负责调用讽刺的明确，隐式和上下文不协调的单词和短语的能力。 BI-ISCA在两个广泛使用的基准数据集上生成最新的结果，以进行讽刺检测任务（Reddit和Twitter）。据我们所知，现有的最新模型都不使用句子间的上下文注意机制来仅使用对话式上下文来检测用户生成的短文本中的讽刺。
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.
