Harnessing Sequence Labeling for Sarcasm Detection in Dialogue from TV Series `Friends'
Aditya Joshi, Vaibhav Tripathi, Pushpak Bhattacharyya, Mark Carman · 2016
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.