# Affection Enhanced Relational Graph Attention Network for Sarcasm Detection
## 1 Introduction  
Sarcasm is a sophisticated linguistic expression and has received a lot of research attention [1, 2, 3]. By irony, the meaning expressed is different from its real one, and such kind of incongruity presents in either an explicit or implicit way. Considering the former example in Figure 1, we can observe an apparent contradiction in the sentence \"I absolutely love to be ignored\" with strong contrastive emotional words \"love\" and \"ignore\", so it is imperative to mine this kind of incongruity expression in sarcastic context. Early works attempted to extract the incongruity expressions in sarcasm detection by capturing the incongruity in-between the words [2, 3, 4] or using lexical features [5]. Recently, some deep learning-based and graph-based methods have achieved significant improvements in sarcasm detection by capturing the incongruity [6, 7, 8, 9, 10]. [PERSON] et al. [6] used a self-attention-based neural network to model the incongruity on word-level explicitly. After that, [PERSON] et al. [7] proposed to model the incongruity between sentence snippets as they contain more semantic information. [PERSON] et al. [8] first attempted to alter BERT's architecture directly and trained it from scratch to build a sarcasm classifier. Later, [PERSON] et al. [9] explored interactive GCNs to interactively learn the incongruity relations of in-modal and cross-modal graphs for determining the significant clues in sarcasm detection. [PERSON] et al. [10] utilized affective and dependency graphs to extract the contradictory implications and incongruity expressions in sarcasm detection, which achieved the best performance at the time. These deep learning models can capture the semantic information well but inevitably lead to misjudgment. Consider the below not sarcastic example in Figure 1, and we observe there still exists a strong contrast between \"great\" and \"dreadful\" in the sentence \"Great food, but the service is dreadful\", which is similar to the former sarcastic example. We will wrongly conclude it as a sarcastic expression if we only consider the semantic information. Once we consider the dependency relationship derived from the parsing tree, we will find that \"great\" is an adjectival modifier of \"food\", and \"dreadful\" is the adjectival complement of \"service\", so the dependency information can help us differentiate the falsely classified sentence. However, direct use of dependencies allocates the same weight to adjacent nodes in the parsing tree, so we apply the dependency relationships as trainable parameters to refine the importance of different relations. Inspired by Ref. [10], which utilizes both affective graph and dependency graph to extract the contradictory expressions, in this paper, we explore a novel network to leverage both affection information and relation information based on contextual representations. The proposed model (ARGAT) first extracts contextual information based on BiLSTM [11] and sends the contextual embeddings to RGAT [12] and GCN [13] layers. The RGAT and GCN layer can be stacked for n layers to extract deeper features. Then, we concatenate the two outputs and put them into a traditional classifier for sarcasm prediction. Experimental results on six benchmark datasets demonstrate that our proposed method achieves the state-of-the-art performance in sarcasm detection. The main contributions of our work can be summarized as follows:  
* We exploit the RGAT network to better learn the syntactic information by incorporating dependency relation information, which contributes to information interaction of structural relevant word pairs with long distances. * A combination model of affective and relational graphs is explored to extract the incongruity in sarcasm detection. * Experimental results on a number of benchmark datasets demonstrate that our proposed method achieves the state-of-the-art performances in sarcasm detection.