# 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 incongruuity. 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. semicontral analysis; sarcasm detection; incongruity extraction; commonsense knowledge; gate mechanism; incongruity cross-attention; deep learning; natural language processing +
Footnote †: journal: Medicalanalysis, opinion mining, dialogue systems, and so on. However, sarcasm, as a subdomain of sentiment analysis, is difficult to be detected even using AI models because of its sophisticated nature in expressions. Specifically, the difficulty of sarcasm detection mainly comes from the ignorance of the context, such as humorous scenes [1], or from the inability to comprehend the incongruity between the literal meaning and the author's real emotion, such as metaphor and sarcasm [2]. It is worth noting that many methods have been studied for sarcasm detection. Some of them are based on the text itself [2; 3; 4; 5; 6], and some are multi-modal methods [7; 8; 9; 10; 11; 12]. One of them deserves special mention since it introduces external commonsense knowledge [13]. Neural networks and attention mechanisms are basically used in previous state-of-the-art methods. Thus far, the recent state-of-the-art approaches use external commonsense knowledge and multi-modality to assist sarcasm detection. Some multi-modal methods also draw on external knowledge [12]. They use the CLIP network to generate the picture's caption as external knowledge, which significantly improves the detection results and performs even more effectively than the image itself for multi-modality sarcasm detection. Commonsense knowledge, the common daily consensus of society on the same thing, helps people understand information [14]. When the brain tries to make sense of information, it often inadvertently concatenates the scattered commonsense knowledge in life [15]. Understanding sarcasm usually requires corresponding commonsense knowledge [16]. Taking Figure 1 as an example, if we do not know that \"homework, work, and practice\" is related to external knowledge such as \"be late\", \"stress,\" and \"get catch up on homework\", it would be difficult to understand the speaker's real attention. We input the text into the pre-trained COMET model to generate the knowledge candidate set.