We explore two methods for representing authors in the context of textual sarcasm detection: a Bayesian approach that directly represents authors' propensities to be sarcastic, and a dense embedding approach that can learn interactions between the author and the text. Using the SARC dataset of Reddit comments, we show that augmenting a bidirectional RNN with these representations improves performance; the Bayesian approach suffices in homogeneous contexts, whereas the added power of the dense embeddings proves valuable in more diverse ones.
REF presented two methods for representing authors in the context of textual sarcasm detection; they show that augmenting a bidirectional RNN with these representations improves performance in sarcasm detection.
Representing Social Media Users for Sarcasm Detection
ArXiv
ArXiv
Computer Science