Automatic Sarcasm Detection: A Survey <s> PROBLEM DEFINITION <s> Sarcasm is generally characterized as a figure of speech that involves the substitution of a literal by a figurative meaning, which is usually the opposite of the original literal meaning. We re-frame the sarcasm detection task as a type of word sense disambiguation problem, where the sense of a word is either literal or sarcastic. We call this the Literal/Sarcastic Sense Disambiguation (LSSD) task. We address two issues: 1) how to collect a set of target words that can have either literal or sarcastic meanings depending on context; and 2) given an utterance and a target word, how to automatically detect whether the target word is used in the literal or the sarcastic sense. For the latter, we investigate several distributional semantics methods and show that a Support Vector Machines (SVM) classifier with a modified kernel using word embeddings achieves a 7-10% F1 improvement over a strong lexical baseline. <s> BIB001 </s> Automatic Sarcasm Detection: A Survey <s> PROBLEM DEFINITION <s> This paper is a novel study that views sarcasm detection in dialogue as a sequence labeling task, where a dialogue is made up of a sequence of utterances. We create a manuallylabeled dataset of dialogue from TV series ‘Friends’ annotated with sarcasm. Our goal is to predict sarcasm in each utterance, using sequential nature of a scene. We show performance gain using sequence labeling as compared to classification-based approaches. Our experiments are based on three sets of features, one is derived from information in our dataset, the other two are from past works. Two sequence labeling algorithms (SVM-HMM and SEARN) outperform three classification algorithms (SVM, Naive Bayes) for all these feature sets, with an increase in F-score of around 4%. Our observations highlight the viability of sequence labeling techniques for sarcasm detection of dialogue. <s> BIB002 </s> Automatic Sarcasm Detection: A Survey <s> PROBLEM DEFINITION <s> Sarcasm is a form of speech in which speakers say the opposite of what they truly mean in order to convey a strong sentiment. In other words,"Sarcasm is the giant chasm between what I say, and the person who doesn't get it.". In this paper we present the novel task of sarcasm interpretation, defined as the generation of a non-sarcastic utterance conveying the same message as the original sarcastic one. We introduce a novel dataset of 3000 sarcastic tweets, each interpreted by five human judges. Addressing the task as monolingual machine translation (MT), we experiment with MT algorithms and evaluation measures. We then present SIGN: an MT based sarcasm interpretation algorithm that targets sentiment words, a defining element of textual sarcasm. We show that while the scores of n-gram based automatic measures are similar for all interpretation models, SIGN's interpretations are scored higher by humans for adequacy and sentiment polarity. We conclude with a discussion on future research directions for our new task. <s> BIB003
We now look at how the problem of automatic sarcasm detection has been defined in past work. The most common formulation for sarcasm detection is a classification task. Given a piece of text, the goal is to predict whether or not it is sarcastic. Thus, the sentence "I love being ignored" is to be predicted as sarcastic while the sentence "I love being pampered" is to be predicted as non-sarcastic. Past work varies in terms of what these output labels are. For example, trying to understand/characterize the relationship between sarcasm, irony, and humor, Reference  considers labels for the classifier as: politics, humor, irony, and sarcasm. Reference  uses a similar formulation and provides pair-wise classification performance for these labels. Other formulations for sarcasm detection have also been reported. Reference BIB002 deviates from the traditional classification definition and models sarcasm detection for dialogue as a sequence labeling task. Each utterance in a dialogue is considered to be an observed unit in this sequence, whereas sarcasm labels are the hidden variables whose values need to be predicted. Thus, instead of predicting "Yeah right" in a conversation on its own, the system takes into account a complete conversation that includes the sentences before and after the "yeah right." Reference BIB001 models sarcasm detection as a sense disambiguation task. The authors state that a word may have a literal sense or a sarcastic sense. For example, the word "amazing" in the sentence "Amazing to see him lose the match in 20 minutes" is used in a sarcastic sense, while in the sentence "Amazing to see a brilliant film such as this" is used in a literal (positive) sense. Their goal is to identify the sense of a word to detect sarcasm. Reference BIB003 models sarcasm interpretation as a monolingual machine translation task. They create a parallel corpus of sarcastic sentences and their non-sarcastic interpretations and use two machine translation systems (based on Moses and RNN, respectively) to obtain non-sarcastic interpretations of the sarcastic sentences. Table 1 summarizes past work in automatic sarcasm detection. While several interesting observations are possible from the table, two are key: (a) tweets are the predominant text form for sarcasm detection, and (b) incorporation of extra-textual context is a recent trend in sarcasm detection.