Automatic Sarcasm Detection: A Survey <s> Transcripts and Dialogue <s> The robust understanding of sarcasm in a spoken dialogue system requires a reformulation of the dialogue manager’s basic assumptions behind, for example, user behavior and grounding strategies. But automatically detecting a sarcastic tone of voice is not a simple matter. This paper presents some experiments toward sarcasm recognition using prosodic, spectral, and contextual cues. Our results demonstrate that spectral and contextual features can be used to detect sarcasm as well as a human annotator would, and confirm a long-held claim in the field of psychology – that prosody alone is not sufficient to discern whether a speaker is being sarcastic. Index Terms: dialogue, user modeling, sarcasm, speech acts <s> BIB001 </s> Automatic Sarcasm Detection: A Survey <s> Transcripts and Dialogue <s> While a fair amount of work has been done on automatically detecting emotion in human speech, there has been little research on sarcasm detection. Although sarcastic speech acts are inherently subjective, humans have relatively clear intuitions as to what constitutes sarcastic speech. In this paper, we present a system for automatic sarcasm detection. Using a new acted speech corpus that is annotated for sarcastic and sincere speech, we examine a number of features that are indicative of sarcasm. The first set of features looks at a baseline of basic acoustic features that have been found to be helpful in human sarcasm identification. We then present an effective way of modeling and applying prosodic contours to the task of automatic sarcasm detection. This approach applies sequential modeling to categorical representations of pitch and intensity contours obtained via k-means clustering. Using a SimpleLogistic (LogitBoost) classifier, we are able to predict sarcasm with 81.57% accuracy. This result suggests that certain pitch and intensity contours are predictive of sarcastic speech. <s> BIB002 </s> Automatic Sarcasm Detection: A Survey <s> Transcripts and Dialogue <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> BIB003
Since sarcasm is a form of verbal irony and is often expressed in the context of a conversation, datasets based on transcripts and dialogue have also been reported. Reference BIB001 uses 131 call center transcripts. Each occurrence of "yeah right" is marked as sarcastic or not. The goal is to identify which "yeah right" is sarcastic. Similarly, Reference BIB002 creates a crowd-sourced dataset of sentences from an MTV show "Daria." Along similar lines, Reference BIB003 reports their results on a manually annotated transcript of the TV Series "Friends." Every "utterance" in a scene is annotated with two labels: sarcastic or not sarcastic.