# Affection Enhanced Relational Graph Attention Network for Sarcasm Detection
## 2 Related Works  
Sarcasm is a complex linguistic phenomenon that has drawn much attention. According to the rich history of research on sarcasm detection, we roughly divide previous related works into four categories: ruled-based, feature-based machine learning, deep learning approaches, and graph-based approaches.  
Figure 1: The figure shows two examples of tweets with dependency. The above sentence is sarcastic tweet and the below is not sarcastic. The words or snippets with incongruity are colored.  
Sarcasm detection was originally solved by using rule-based approaches; such approaches aim to identify sarcasm with fixed patterns. [PERSON] et al. [3], [PERSON] and [PERSON] [14], [PERSON] et al. [15] distinguished irony sentences via lexical characteristics, such as the co-occurrence of positive and negative sentiments and hashtags. [PERSON] and [PERSON] [2], [PERSON] et al. [16] attempted to capture context incongruity by including extra-linguistic information. [PERSON] and [PERSON] [5], [PERSON] et al. [17] augmented traditional linguistic and stylistic features for sarcasm detection with the representative features obtained from readers' movement when sarcasm occurred.  
However, rule-based approaches were insufficient to capture complex sarcastic texts. Researchers began to exploit feature-based machine learning methods to solve the problem [18; 19; 20]. [PERSON] and [PERSON] [18] used n-grams to search for a set of recurrent words carrying sarcasm information. Various machine learning methods were explored in [PERSON] and [PERSON] [19], namely, decision tree (DT), random forest (RF), support vector machine (SVM), and Naive Bayes (NB). [PERSON] [20] used both structural features, such as punctuation mark frequency, tweet length, uppercase character amount, and affective characteristics to detect sarcasm.  
With the development of neural networks, modern deep-learning methods such as CNNs [21; 22], RNNs [23; 24], and Recurrent Convolutional Neural Networks(RCNNs) [25] were gradually applied in sarcasm detection. [PERSON] et al. [26], [PERSON] and [PERSON] [27] introduced the attention mechanism to neural networks to detect sarcastic comments and revealed that the attention mechanism enhances the performance. These neural networks mainly utilized semantic information, and little structural information was involved.  
A new research brand called graph neural networks was proposed to obtain global structural information and the non-continuous relation over long distances. [PERSON] et al. [9], [PERSON] et al. [28] used GCN to capture the features of the global knowledge in the satire context, while [PERSON] and [PERSON] [29] applied a GAT to model a dependency tree of sarcastic expression. [PERSON] et al. [10] further leveraged both affective and contextual features to draw long-range inconsistent terms over the context for sarcasm detection. However, direct application of dependence may inevitably introduce noisy information and inferiorly in modeling long-distance or disconnected words in the dependency tree. Inspired by the works mentioned above, we propose a novel model ARGAT, which combines the RGAT with GCN to integrate structural and affective information.