Creating and Characterizing a Diverse Corpus of Sarcasm in Dialogue
This work addresses the problem of sarcasm detection for NLP researchers by providing a more reliable corpus, though it is incremental as it builds on existing methods for a specific domain.
The researchers tackled the challenge of constructing a high-quality corpus for sarcasm in dialogue by creating a large-scale, diverse dataset from online debate forums and developing methods to classify sarcasm types like rhetorical questions and hyperbole. They achieved higher precision and F-scores than previous work using lexico-syntactic cues and supervised learning with simple features.
The use of irony and sarcasm in social media allows us to study them at scale for the first time. However, their diversity has made it difficult to construct a high-quality corpus of sarcasm in dialogue. Here, we describe the process of creating a large- scale, highly-diverse corpus of online debate forums dialogue, and our novel methods for operationalizing classes of sarcasm in the form of rhetorical questions and hyperbole. We show that we can use lexico-syntactic cues to reliably retrieve sarcastic utterances with high accuracy. To demonstrate the properties and quality of our corpus, we conduct supervised learning experiments with simple features, and show that we achieve both higher precision and F than previous work on sarcasm in debate forums dialogue. We apply a weakly-supervised linguistic pattern learner and qualitatively analyze the linguistic differences in each class.