Just Like a Human Would, Direct Access to Sarcasm Augmented with Potential Result and Reaction (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.

## introduction sarcasm, as subtle figures of speech, serves many communicative purposes in human daily life @xcite , 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." the sarcastic text is typically characterized as an incongruity between the positive surface and negative situation @xcite . 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 @xcite @xcite @xcite . for example, several early sd studies express the incongruity by extracting positive-negative pairs from observable text content, such as rule-based method @xcite and neural networks with co-attention tricks @xcite . 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 @xcite , to estimate the implied affective correlations among words and phrases of samples @xcite . additionally, the sardeck method @xcite employs the pre-trained commonsense reasoning tool comet @xcite 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. 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 @xcite @xcite , we are agreeable with two significant viewpoints. first, the negative situations of sarcasm are mostly associated with certain social events @xcite , and human beings can often easily identify the events with the background information in the brain. second, from the direct access view @xcite @xcite , 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 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 @xcite relations inferred by the auxiliary commonsense reasoning tool comet @xcite , and then integrate the sample with them to generate an augmented one, dubbed as event-augmented sample. by analogy to @xcite , 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 @xcite , 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 @xcite , 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: ## the proposed sd-aprr method in this section, we briefly describe the task definition of sd and the commonsense reasoning tool comet. we then introduce the proposed sd-aprr method in more detail. for clarity, we summarize the important notations in 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 (s i , y i ), where brief description of comet. the comet @xcite 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 atomic sires of speakers; and @xcite 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 @xcite as the backbone, which contains 24 layers, 1024-dimensional hidden embeddings, and 16 heads for self-attention. 2 then it was fine-tuned over atomic 20 20 . ## conclusion and limitations 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.