# Sarcasm Detection Base on Adaptive Incongruity Extraction Network and Incongruity Cross-Attention
## 2 Related Work  
Sarcasm has become increasingly prevalent on the Internet with the rise of various social media. This language phenomenon has been the subject of extensive cognitive psychology and linguistics research. However, the study of it is in the infancy of computational linguistics, and more work is still required. Sarcasm detection was considered as standard classification of text in some previous research. [PERSON] et al. [27] wrote a very comprehensive overview of sarcasm detection. Existing research methodologies may be loosely categorized into three groups: (1) methods based on rules and machine learning, (2) deep learning-based neural networks, and (3) methods that introduce external knowledge and other multi-modal methods on top of neural networks. The rule-based approach that tried to find the rules of linguistics has been very classical and traditional. For example, the method of the emotional dictionary establishes the emotional dictionary of sarcasm [28; 29; 30] and the representative indicators. Meanwhile, feature engineering methods are also used to make use of different features for recognition, such as grammatical patterns, n-gram [21], readability and flips [31], word frequency [32], etc. Generally, traditional machine learning uses a combination of pattern features and semantic features. Among these many semantic features, incongruity-based features have been researched the most. Incongruity information is often found by looking for positive words in negative contexts or vice versa [22; 23; 24]. [PERSON] et al. [3] presented a bootstrapping algorithm for extracting incongruity, automatically learning phrases related to positive sentiments and negative situations. They obtain lists of positive emotion items, negative activities, and states to detect sarcasm, and the phrases they obtained were restricted to particular syntactic structures only when the contrasting phrases were in a highly constricted context. [PERSON] et al. [4] developed a paradigm based on explicit and implicit incongruity with positive-negative pattern-based features, which indicates that extracting the incongruity produced a significant enhancement compared to previous ways of feature engineering. These rule-based extractions of incongruity require manually designed rules for capturing, which fails to capture those incongruities beyond the rules. Since deep learning has achieved outstanding performance in various domains, it has also achieved excellent results in sarcasm detection. [PERSON] et al. [19] are among the earliest researchers to demonstrate the efficacy of word embeddings for sarcasm detection. Text-CNN, GRU, LSTM, RNN, Bi-LSTM, and other network models have also been widely used in sarcasm detection. They can capture information from text sequences effectively. [PERSON] and [PERSON] integrate fully connected neural networks and Long Short-Term Memory (LSTM) to extract sequential features from sentences [33]. In their subsequent work, [PERSON] and [PERSON] enhance model accuracy by including the user's mood information and contextual information into a neural architecture with two CNN layers and one LSTM layer [1]. [PERSON] uses LSTM coupled with the MLP model to obtain the semantic information of the whole sequence [34]. All those previous approaches to deep learning can manage the sequence effectively. However, they are unable to focus on the key items sufficiently as the attention mechanism does, which makes it difficult to extract incongruities. In the field of NLP, the core concept of attention is the soft selection of a sequence of words based on their relative significance to the current task. Attention is also prevalent in various NLP research, including sentiment classification [35; 36] and aspect-level sentiment analysis [37; 38]. In [2; 5], they use a similar approach based on an attention mechanism to extract incongruity. [PERSON] et al.