  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.
