Just Like a Human Would, Direct Access to Sarcasm Augmented with Potential Result and Reaction
Year: 2023

Abstract:
Sarcasm, as a form of irony conveying mockery and contempt, has been widespread in social media such as Twitter and Weibo, where the sarcastic text is commonly characterized as an incongruity between the surface positive and negative situation. Naturally, it has an urgent demand to automatically identify sarcasm from social media, so as to illustrate people’s real views toward specific targets. In this paper, we develop a novel sarcasm detection method, namely Sarcasm Detector with Augmentation of Potential Result and Reaction (SD-APRR). Inspired by the direct access view, we treat each sarcastic text as an incomplete version without latent content associated with implied negative situations, including the result and human reaction caused by its observable content. To fill the latent content, we estimate the potential result and human reaction for each given training sample by [xEffect] and [xReact] relations inferred by the pre-trained commonsense reasoning tool COMET, and integrate the sample with them as an augmented one. We can then employ those augmented samples to train the sarcasm detector, whose encoder is a graph neural network with a denoising module. We conduct extensive empirical experiments to evaluate the effectiveness of SD-APRR. The results demonstrate that SD-APRR can outperform strong baselines on benchmark datasets.

heading: 1 Introduction
text: Sarcasm, as subtle figures of speech, serves many communicative purposes in human daily life (Ivanko and Pexman, 2003), commonly used to criticize an individual. Refer to the formal description of sarcasm from the Oxford English Dictionary:1
“A way of using words that are the opposite of what you mean in order to be unpleasant to somebody or to make fun of them.”
∗* Corresponding Author 1https://www.oed.com
The sarcastic text is typically characterized as an incongruity between the positive surface and negative situation (Riloff et al., 2013; Liu et al., 2022). For example, as an obvious sarcasm “I love working for six hours every day for free”, its surface meaning tends to be positive, conveyed by the sentiment word “love”, but it corresponds to a negative situation “work for free”, conveying people’s complaint.
Detecting sarcasm from social media is a significant task due to the universal existence of sarcasm, but its complicated nature makes the task challenging. To resolve this task, the community has recently proposed a number of Sarcasm Detection (SD) methods, whose major idea is to capture the incongruity characteristic of sarcasm (Joshi et al., 2017; Xiong et al., 2019; Pan et al., 2020; Agrawal et al., 2020; Li et al., 2021b; Lou et al., 2021). For example, several early SD studies express the incongruity by extracting positive-negative pairs from observable text content, such as rule-based method (Joshi et al., 2017) and neural networks with co-attention tricks (Xiong et al., 2019; Pan et al., 2020). Unfortunately, those methods cannot accurately capture the negative situations, which are mostly implied and associated with contexts and background information. To alleviate this issue, the recent arts of SD express the negative situations with external knowledge bases. From the perspective of sentiments, some SD methods employ auxiliary affective lexicons, e.g., SenticNet (Cambria et al., 2020), to estimate the implied affective correlations among words and phrases of samples (Agrawal et al., 2020; Lou et al., 2021). Additionally, the SarDeCK method (Li et al., 2021b) employs the pre-trained commonsense reasoning tool COMET (Hwang et al., 2021) to infer the relations behind samples as their implied situations. Despite the promising performance, their expressions of implied negative situations are still a bit abstract and impalpable.
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As complicated figures of speech, we are particularly interested in how do human beings accurately identify sarcasm? Through referring to the prior psychological, cognitive, and linguistic literature (Gibbs, 1986; W.Gibbs, 2002; Ivanko and Pexman, 2003), we are agreeable with two significant viewpoints. First, the negative situations of sarcasm are mostly associated with certain social events (Pickering et al., 2018), and human beings can often easily identify the events with the background information in the brain. Second, from the direct access view (Giora and Fein, 1999; W.Gibbs, 2002; Ivanko and Pexman, 2003), human beings are likely to directly understand the whole sarcastic text with both literal meanings and implied negative situations, which can be easily captured by them.
Based on the analysis, what we expect is to develop a novel SD method by simulating the way of human thinking. Inspired by the direct access view, we treat each sarcastic text as an incomplete version without latent content associated with implied negative situations. We can use the associated social events to express the negative situations due to their strong connection. Further, we assume the social events can be mainly expressed by the potential results and human reactions that the events produced (see examples in Table 1). Accordingly, for each given sample we can estimate its potential result and human reaction by pre-trained commonsense reasoning tools (acted as background information), and then integrate the observable text content with them as an augmented sample (acted as the whole text). Finally, we can use those augmented samples to train the sarcasm detector (just like a human would).
Upon these ideas, we propose a novel SD method, namely Sarcasm Detector with Augmentation of Potential Result and Reaction (SD-APRR). Specifically, we estimate the potential result and human reaction for each training sample by [xEffect] and [xReact] relations inferred by the auxiliary commonsense reasoning
tool COMET (Hwang et al., 2021), and then integrate the sample with them to generate an augmented one, dubbed as event-augmented sample. By analogy to (Lou et al., 2021; Liang et al., 2022), we assume that the syntactic information of eventaugmented samples can intuitively imply the incongruity of sarcasm. Accordingly, we transform each event-augmented sample into a dependency graph (Nivre, 2003), and suggest a graph-based encoder to generate sample embeddings. Additionally, to resolve the noisy results and reactions inferred by COMET, we suggest a denoising module with the dynamic masking trick (Yang et al., 2021), enabling to improve the quality of sample embeddings. With those embeddings, a single-layer MLP is used as the sarcastic classifier finally. To examine the effectiveness of SD-APRR, we conduct extensive experiments on benchmark datasets. The empirical results demonstrate that SD-APRR can outperform the existing baseline methods.
The contributions of this work can be summarized as follows:
• We propose a novel SD method, named SDAPRR, with event-augmented samples formed by the auxiliary commonsense reasoning tool COMET.
• We suggest a graph-based encoder with a denoising module, enabling to generate strong sample embeddings.
• The experimental results indicate that SDAPRR can achieve competitive performance compared with existing baselines.

heading: 2 Related Works

heading: 2.1 Sarcasm Detection
text: Early SD methods are mostly based on special rules and evidence (Maynard and Greenwood, 2014; Bharti et al., 2015; Riloff et al., 2013). For instance, the study (Maynard and Greenwood, 2014) treats the hashtag sentiment as the key indicator of sarcasm since the hashtags are usually taken
to highlight sarcasm in Tweets; and other methods employ various evidence, such as parser-based negative phrase matching, interjections (Bharti et al., 2015), and positive-negative word pairs (Riloff et al., 2013). Some other methods form incongruity-specific embeddings for sarcastic texts, such as shape and pointedness of words (Ptáček et al., 2014), extensions of words (Rajadesingan et al., 2015), and unexpectedness (Reyes et al., 2012).
Due to the success of neural networks, the mainstream SD methods nowadays apply them to capture the incongruity between positive surface and negative situations within the sarcastic text. Early methods mainly capture the incongruity from the observable text content (Tay et al., 2018; Xiong et al., 2019; Pan et al., 2020). For instance, the methods of (Xiong et al., 2019; Pan et al., 2020) extract positive-negative word pairs and phrase pairs with co-attention tricks. However, those methods cannot fully understand the negative situation due to its implicit nature. To resolve this issue, the recent methods employ external resources to capture negative situations and further incongruities of sarcastic texts (Agrawal et al., 2020; Lou et al., 2021; Li et al., 2021b; Liu et al., 2022). For example, the ADGCN method (Lou et al., 2021) employs the affective lexicon SenticNet (Cambria et al., 2020) to represent intra-sentence affective relations; and the DC-Net method (Liu et al., 2022) exploits sentiment lexicon to separate literal meanings from texts and further estimates sentiment conflicts. Orthogonal to the aforementioned methods, our SD-APRR forms augmented samples by commonsense reasoning and treats the augmented ones as the whole versions of sarcastic texts from the direct access view (Giora and Fein, 1999; W.Gibbs, 2002; Ivanko and Pexman, 2003).

heading: 2.2 Commonsense Knowledge Graph
text: Large-scale commonsense knowledge graphs (Lin et al., 2019; Yin et al., 2022) can conduct reasoning for texts to infer the commonsense knowledge behind them, and they have been widely applied to a wide range of natural language processing tasks, such as dialogue generation (Sabour et al., 2022), relation classification (Hosseini et al., 2022), and emotion recognition (Li et al., 2021a). To our knowledge, some representatives include ConceptNet (Speer et al., 2017), ATOMIC (Sap et al., 2019), and TransOMCS (Zhang et al., 2020). The Con-
ceptNet contains 3.4M entity-relation tuples and about 90% of these tuples are taxonomic and lexical knowledge, resulting in relatively smaller commonsense portion. The recent ATOMIC contains 880K of tuples with 9 relations, which covers social commonsense knowledge including effects, needs, intents, and attributes of the actors in an event. In addition, the TransOMCS contains 18.5M tuples collected from various web sources, and the relations are similar to the ConceptNet.

heading: 3 The Proposed SD-APRR Method
text: In this section, we briefly describe the task definition of SD and the commonsense reasoning tool COMET. We then introduce the proposed SDAPRR method in more detail. For clarity, we summarize the important notations in Table 2.
Task definition. Given N labeled training samples, the goal of SD is to induce a sarcasm detector enabling to distinguish whether a text sample belongs to sarcasm or not. Formally, each training sample is represented by (si, yi), where si = {wi1, · · · , wiM} is the raw text and yi ∈ Y is the category label. The label space is commonly defined as Y = {sarc, non-sarc}.
Brief description of COMET. The COMET (Hwang et al., 2021) is a pre-trained commonsense reasoning tool, which can infer various kinds of commonsense relations associated with the related event of a given text. It totally contains 23 commonsense relations defined in ATOMIC2020. For examples, [xWant] describes post-condition de-
sires of speakers; and [xReason] gives a postfact explanation of the cause of an event. Here, we specially introduce [xEffect] and [xReact], where [xEffect] provides social results that may occur after an event, while [xReact] provides the speakers’ emotional reactions to an event. The outputs of the two relations can be directly used as the auxiliary augmentation in SD-APRR.
We declare that the COMET takes the large version of BART (Lewis et al., 2020) as the backbone, which contains 24 layers, 1024-dimensional hidden embeddings, and 16 heads for self-attention.2 Then it was fine-tuned over ATOMIC2020 .

heading: 3.1 Overview of SD-APRR
text: As depicted in Fig.1, our SD-APRR mainly consists of three components. (1) Event-augmented samples generation: For each raw text si, we employ COMET to infer its result eri and human reaction ehi , and then concatenate them to form the corresponding event-augmented sample sei . (2) Maksed graph-based encoder: For each event-augmented sample sei , we transform it into a dependency graph Gi, and encode Gi as the sample embedding zi by leveraging a graph neural network encoder with dynamic masking. (3) Sarcastic classifier: With zi, we predict the category label by employing a singlelayer MLP finally. In the following, we introduce each component of SD-APRR in more detail.
2https://huggingface.co/facebook/bart-large

heading: 3.2 Event-Augmented Samples Generation
text: For each raw text si = {wi1, · · · , wiM}, we feed it into the pre-trained COMET with the [xEffect] and [xReact] relations, and treat the outputs eri = {wi1, · · · , wiM} and ehi = {w̃i1, · · · , w̃iM̃} as the result and human reaction of the implied social event behind si. We then concatenate them to form its event-augmented version. For semantic coherence, we further leverage two linkers lr and lh, where lr denotes “then may” for eri and lr denotes “and I feel” for ehi . Accordingly, the final event-augmented sample is formed by sei = si ⊕ le ⊕ eri ⊕ lh ⊕ ehi , where ⊕ denotes the concatenation operator, and it totally contains M e word tokens, where M e = M +M + M̃ + 5. We have shown the example in Fig.1.

heading: 3.3 Masked Graph-based Encoder
text: Given event-augmented samples {sei}Ni=1, we suggest a masked graph-based encoder to induce their embeddings {zi}Ni=1.

heading: 3.3.1 Constructing Graphs of Samples
text: By analogy to (Lou et al., 2021; Liang et al., 2022), we assume that the syntactic information of eventaugmented samples can intuitively imply the incongruity of sarcasm. Accordingly, we transform each sei into an undirected graph Gi = {Vi, Ei} with the off-the-shelf dependency parsing tool,3 where Vi
3In this work, we employ the off-the-shelf syntax toolkit available at the website “https://spacy.io/”.
is the set of nodes, i.e., the tokens occurring in sei , and Ei is the set of edges computed by dependency parsing. Define Ai ∈ {0, 1}Me×Me as its corresponding adjacency matrix, and 1/0 denotes the component corresponds to an edge or not. Besides, each node is with self-loop.

heading: 3.3.2 Initializing Node Embeddings
text: For each Gi, we initialize its node embeddings H
(0) i = [h (0) i1 , · · · ,h (0) iMe ] ⊤ by leveraging a singlelayer Bi-LSTM (Hochreiter and Schmidhuber, 1997). Specifically, we represent the nodes Xi = [xi1, · · · ,xiMe ]⊤ by the pre-trained GloVe word embeddings, and then feed Xi into the Bi-LSTM as follows:
H (0) i = Bi-LSTM(Xi;Wb), (1)
where Wb is the trainable parameter of Bi-LSTM.

heading: 3.3.3 Learning Sample Embeddings with Dynamic Masking
text: Given each pair {Gi,H(0)i }, we optimize the node embeddings H(l)i = [h (l) i1 , · · · ,h (l) iMe ]
⊤ by a Llayer graph neural network encoder with dynamic masking (Yang et al., 2021), and then form the final sample embedding zi by leveraging the readout operator with Hi.
To be specific, the learning process of node embeddings for each layer is formulated below:
h (l) ij =ReLU ( W(l)n m (l) ij [ h (l−1) ij ⊕ h (l−1) N (ij) ]) ,
j = 1, · · · ,M e, l = 1, · · · , L, (2)
where Wn = {W(l)n }Ll=1 are the trainable parameters; mij ∈ [0, 1] is the mask weight of the j-th node, used to capture the possible noisy eri and e h i inferred by COMET; N (ij) denotes the neighbor set of the j-th node; and h(l−1)N (ij) =∑ k∈N (ij)m (l−1) ik h (l−1) ik is the weighted sum of the neighbors of the j-th node. The update process of the mask weights for each layer is formulated below:
m (l) ij =Sigmoid ( W(l)m ĥ (l−1) ij ⊕W (l) f h (l−1) N (ij) )
j = 1, · · · ,M e, l = 1, · · · , L, (3)
where Wm = {W(l)m }Ll=1 and Wf = {W (l) f }Ll=1 are the trainable parameters; and ĥ(l−1)ij = m (l−1) ij h (l−1) ij .
After obtaining the node embeddings H(L)i of the last layer, we can form the sample embedding zi by leveraging the readout operator as follows:
zi = 1
M e ∑Me i=1 h (L) i (4)

heading: 3.4 Sarcastic Classifier and Training Objective
text: Given the sample embeddings {zi}Ni=1, we employ a single-layer MLP as the sarcastic classifier. For each zi, we predict its category label ŷi by the following equation:
ŷi = Softmax (Wczi) , (5)
where Wc is the trainable parameter of the sarcastic classifier.
Consider N training pairs {(zi, yi)}Ni=1, we can formulate the full objective of SD-APRR with respect to all trainable parameters W = {Wb,Wn,Wm,Wf ,Wc}:
L(W) = N∑
i=1
LCE(yi, ŷi) + λ∥W∥2, (6)
where LCE is the cross-entropy loss; ∥ · ∥ denotes the ℓ2-norm; and λ ∈ [0, 1] is the regularization coefficient.

heading: 4 Experiment

heading: 4.1 Experimental Settings
text: Datasets. To thoroughly evaluate the performance of SD-APRR, we conduct experiments on four publicly available SD datasets with different scales. Their statistics of are shown in Table 3, and they are briefly described below:
• SemEval18 is collected in SemEval 2018 Task 3 Subtask A (Van Hee et al., 2018).
• iSarcasm (Oprea and Magdy, 2020) consists of tweets written by participants of an online survey and thus is for intented sarcasm detection.
• Ghosh (Ghosh and Veale, 2016) is collected from Twitter and leverages hashtag to automatically annotate samples.
• IAC-V2 (Abbott et al., 2016) is sourced from online political debates forum.4 Compared with other datasets, the samples of IAC-V2 are relatively longer and more normative.
4http://www.4forums.com/political/
Baselines. We select a number of recent baseline methods for comparison. They are briefly described below:
• NBOW: A traditional SD method that represents samples by the averages of word embeddings.
• Bi-LSTM: A SD method that sequentially encodes sarcastic texts with Bi-LSTM.
• SIARN and MIARN (Tay et al., 2018): Two RNN-based SD methods that capture the incongruity by using the single-dimensional and multi-dimensional intra-sentence attentions, respectively. We implement in-house codes.
• SAWS5 (Pan et al., 2020): A CNN-based SD method that cuts each text sample into snippets and uses self-attention to re-weight them.
• ADGCN6 (Lou et al., 2021): A GCN-based SD method that builds affective and dependency graphs with SenticNet to capture the incongruity in a long distance.
• DC-Net7 (Liu et al., 2022): A BERT-based SD method that respectively encodes literal
5https://github.com/marvel2120/SAWS 6https://github.com/HLT-HITSZ/ADGCN 7https://github.com/yiyi-ict/ dual-channel-for-sarcasm
meanings and implied meanings by the external sentiment lexicon.
• SarDeCK8 (Li et al., 2021b) A BERT-based SD method that uses the COMET to derive dynamic commonsense knowledge and fuses the knowledge to enrich the contexts with attention.
Implementation details. In the experiments, except the BERT-based methods, we apply the 300- dimensional GloVe embeddings9 to represent the words initially. The dimension of the Bi-LSTM output is set to 300, and the layer number of the masked graph-based encoder is set to 3. For all neural network-based methods, the batch size is set to 32. We take Adam as the optimizer, and the learning rate is set to 0.001. The regularization coefficient λ is set to 0.01. Besides, we use the Xavier Uniform to initialize the parameters. For the BERTbased methods, the number of training epochs is set to 6, while for other methods, the epoch number is fixed to 100 with an early stopping mechanism (Lou et al., 2021). In terms of all datasets, the splitting of training and testing is shown in Table 3. We independently run all comparing methods 5 times and report the average results.
8https://github.com/LeqsNaN/SarDeCK 9https://nlp.stanford.edu/projects/glove/
Evaluation metrics. By convention, we employ Accuracy and Macro-F1 as the evaluation metrics in our experiments.

heading: 4.2 Results and Analysis
text: The main results of all comparing methods are reported in Table 4, and we draw the following observations: (1) First, it can be clearly seen that our SD-APRR can achieve the highest scores of both Accuracy and Macro-F1 in most settings, where it ranks the first over SemEval18, iSarcasm, and IACV2, and ranks the second over Ghosh. (2) Second, we observe that SD-APRR mostly outperforms the recent strong baseline SarDeCK, which also employs COMET to generate auxiliary commonsense relations. A major difference between SarDeCK and SD-APRR is that the former integrates training samples with their corresponding commonsense results of COMET at the embedding level, while the latter treats the augmentations of raw training texts and inferred commonsense results of COMET as the whole raw texts. So the improvements to SarDeCK indirectly indicate that the direct access view may be a better perspective for SD. (3) Third, compared with ADGCN that is also based on graph neural networks, our SD-APRR achieves significant improvements over all datasets. This indicates that leveraging contextually inferred results and reactions can be a more efficient way for SD than
leveraging context-free affective lexicons in a static way. (4) Finally, SD-APRR, ADGCN, DC-Net, and SarDeCK consistently perform better than NBOW, Bi-LSTM, SIARN, MIARN, and SAWS, the methods without external resources. The results support the previous statement that understanding sarcasm heavily relies on human background information.

heading: 4.3 Ablation Study
text: We conduct ablation studies to examine the effectiveness of the augmentations of results, augmentations of human reactions, and the denoising module. The results are reported in Table 5. Overall, when removing the results (w/o Result) and the reactions (w/o Reaction), the performance of SD-APRR show a decline on all datasets. This indicates that the potential results enable the SD-APRR to have extra explainable contexts to understand the negativity inside the negative situations. Meanwhile, human reactions provide explicit emotional clues that can be related to the negative situations during graph learning. However, when removing the denoising module (w/o Masking), the performance of SDAPRR decreases across the IAC-V2 dataset. This is because samples in the Ghosh are short texts, and their syntactical information may not be accurately captured, leading the masked graph-based encoder skips nodes related to the sarcasm by mistake.
Additionally, we replace the masked graphbased encoder with BERT (Devlin et al., 2019), and further compare this BERT-based version of SD-APRR with its ablative versions (w/o Result and w/o Reaction). Due to the space limitation, we report the results on two datasets, i.e., Ghosh with relatively more training samples and IAC-V2 with longer text lengths. The results are shown in Table 6. We can observe that the full version performs the best compared with the ablative versions. These results further indicate the augmentations of results and human reactions inferred by COMET can improve the classification performance even
Table 7: The visualization of mask weights of example training samples. The words in red are with much lower values of mask weights.
ID Texts
1 People that start construction work before 7am on a Sunday need to just f**k off and die thanks, then may get fired from job and I feel angry. 2 Love to live in this very cool country where a ten thousand dollar medical bill is low, then may go to see a doctor and I feel happy. 3 the most exciting thing to look forward to this weekend. work work work, then may get a promotion and I feel happy.
1 2 3 4 5 #layers
65
67
69
71
73
75
Pe rf or m an ce
SemEval18
1 2 3 4 5 #layers
55
60
65
70
75
80
85
Pe rf or m an ce
iSarcasm
1 2 3 4 5 #layers
75
77
79
81
83
85
Pe rf or m an ce
Ghosh
1 2 3 4 5 #layers
70
72
74
76
78
80
Pe rf or m an ce
IAC-V2
Accuracy Macro-F1
Figure 2: The impact of layer numbers of the masked graph-based encoder.
with a different encoder.

heading: 4.4 The Impact of Layer Numbers of the Masked Graph-based Encoder
text: We now investigate the impact of the layer number L of the masked graph-based encoder across benchmark datasets. We present the results with different values of L ∈ {1, 2, 3, 4, 5} in Fig 2. We can observe that SD-APRR performs the best results across the SemEval18 dataset when L = 1, while achieving the best results across the other datasets when L = 3. The reason may be that the positive surfaces and the negative situations in the SemEval18 dataset are close to each other on the dependency graph, so the two terms can be associated together through low-order message-passing. While for the other three datasets, SD-APRR requires higher-order message passing to model the incongruity between the two terms. In practice, we suggest L = 3 as the default setting.

heading: 4.5 Visualization of Mask Weights.
text: To qualitatively visualize the impact of mask weights, we randomly select several examples and
show the words with lower mask weights of the final layer of the masked graph-based encoder. The visualization is shown in Table 7. We use the red color to demonstrate the word tokens with lower mask weights. From the table, we observe that the encoder can effectively eliminate semantically irrelevant tokens, such as "gets fired" and "see doctor", and wrong speakers’ reactions, such as the term of "happy" in the second and the third cases. Besides, we observe that some sarcasm-irrelevant parts in the original texts can also be captured, e.g., the stop words "on", "to", "is".

heading: 5 Conclusion and Limitations
text: In this paper, we propose a novel SD method, entitled SD-APRR, which expresses negative situations of sarcasm by the potential results and human reactions of the associated events. We employ the COMET to estimate the results and human reactions, and form event-augmented samples with them. We employ those augmented samples as the whole sarcastic texts from the direct access view. We suggest a masked graph-based encoder, enabling to generate discriminative sample embeddings. Experimental results demonstrate that our SD-APRR can achieve competitive performance compared with the existing baseline methods.
We demonstrate two limitations: (1) The datasets used in this work are mostly collected from social media. In the future, we plan to collect sarcastic texts from various sources, such as the literature and films, and conduct more experiments with them. (2) Our exploration of sarcasm theories still has some space to improve. Though the incongruity theory is the mainstream in the community, there are other theories worthy to investigate in the future.

heading: Acknowledgment
text: We would like to acknowledge support for this project from the National Natural Science Foun-
dation of China (No.62076046), and the Young Scientists Fund of the National Natural Science Foundation of China (No.62006034).

heading: A For every submission:
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heading: Future Work
text: In the future, we plan to collect sarcastic texts from various sources, such as the literature and films, and conduct more experiments with them. (2) Our exploration of sarcasm theories still has some space to improve. Though the incongruity theory is the mainstream in the community, there are other theories worthy to investigate in the future