Sarcasm Detection Base on Adaptive Incongruity Extraction Network and Incongruity Cross-Attention

Abstract

Sarcasm is a linguistic phenomenon indicating a difference between literal meanings and implied intentions. It is commonly used on blogs, e-commerce platforms, and social media. Numerous NLP tasks, such as opinion mining and sentiment analysis systems, are hampered by its linguistic nature in detection. Traditional techniques concentrated mostly on textual incongruity. Recent research demonstrated that the addition of commonsense knowledge into sarcasm detection is an effective new method. However, existing techniques cannot effectively capture sentence “incongruity” information or take good advantage of external knowledge, resulting in imperfect detection performance. In this work, new modules are proposed for maximizing the utilization of the text, the commonsense knowledge, and their interplay. At first, we propose an adaptive incongruity extraction module to compute the distance between each word in the text and commonsense knowledge. Two adaptive incongruity extraction modules are applied to text and commonsense knowledge, respectively, which can obtain two adaptive incongruity attention matrixes. Therefore, each of the words in the sequence receives a new representation with enhanced incongruity semantics. Secondly, we propose the incongruity cross-attention module to extract the incongruity between the text and the corresponding commonsense knowledge, thereby allowing us to pick useful commonsense knowledge in sarcasm detection. In addition, we propose an improved gate module as a feature fusion module of text and commonsense knowledge, which determines how much information should be considered. Experimental results on publicly available datasets demonstrate the superiority of our method in achieving state-of-the-art performance on three datasets as well as enjoying improved interpretability.

Keywords

• sentiment analysis
• sarcasm detection
• incongruity extraction
• commonsense knowledge
• gate mechanism
• incongruity cross-attention