# Sarcasm Detection Base on Adaptive Incongruity Extraction Network and Incongruity Cross-Attention
## 2 Related Work
[2] proposed the intra-attention method to detect the incongruity within a sentence and between different words and finally obtain the semantic incongruity information of the whole sentence. [PERSON] et al. [5] improved the method in [2], increased the learnable attention parameters, and suggested an innovative self-matching module to detect sentence incongruity. Various research on sarcasm detection focuses on the extraction of incongruity, whether the study employed a rule-based, machine learning, or deep learning approach or not. In [3; 4; 19; 21; 22; 23; 24; 25], they proposed rule-based extractions of incongruity that required manually designed rules for capture, as only incongruity that meets the related rules can be identified. With the wide application of deep learning, the approaches of attention mechanisms have also obtained good performance in capturing incongruity [2; 5]. These methods, which obtain weights automatically, outperform previous rule-based ones. We suggested an adaptive incongruity extraction module inspired by the notion of searching for words' contrast and incongruity [2; 3; 21; 25]. However, their methods consider similarity and assign higher weight scores to highly similar words, which cannot effectively capture the incongruity information within a sentence. Incongruity can be more effectively captured when phrases with opposite sentimental polarity are given higher attention scores. We propose an adaptive incongruity extraction module that calculates the Euclidean distance between words to address this issue. Terms with different sentiment polarities can get higher weight scores, which can better extract the incongruuous semantic information in the sentence. Sarcasm detection is becoming increasingly popular in studies of dialogue systems and Multi-modal. Though different from our study employed in areas of brief texts, much of their research is still very enlightening to our future work. [PERSON] et al. [39] propose a complex-valued fuzzy network to capture the ambiguity and uncertainty of sarcastic human discourse. [PERSON] et al. [40] introduced a deep learning-based emoji-aware multitask learning framework and a Gated Emoji-aware Multimodal Attention mechanism. Their methods make full use of emojis for sarcasm detection, which is novel and worth a further study in the future. [PERSON] et al. [41] have compiled a dataset of Chinese sarcasm with contextual information. In the meantime, they offer a deep learning model for sarcasm detection based on a retrospective strategy. [PERSON] et al. [42] utilize modality-specific sarcastic information with in-modal graphs of text and image modality and a cross-modal graph to extract contradicting expressions between text-modality and image-modality. Sometimes, whether a sentence contains sarcasm or not can only be known by means of context. Some researchers [1; 43; 44; 45; 46] have also investigated the type of dialogue with the context. This type of sarcasm detection has become popular in recent years. The most recent approaches for sarcasm detection are based on the pre-trained BERT model with external knowledge or multi-modal information. [PERSON] et al. [13] suggested a new method to introduce corresponding commonsense knowledge for each text through the COMET model [26]. They proposed three strategies to select external knowledge, which brings new solutions to sarcasm detection. They also demonstrated the significance of commonsense knowledge in sarcasm detection. Often, it is difficult to understand the correct meaning of sarcasm from the literal words alone. Only by associating relevant commonsense knowledge can we better comprehend the reality, which is consistent with the cognitive process of humans. However, their work contains a few flaws in its application of external commonsense knowledge [13]. In [8; 10; 11; 47; 12], they studied multi-modal sarcasm detection, [PERSON] et al. [12] used the CLIP model to generate caption information for each image.