  We introduce a deep neural network for automated sarcasm detection. Recent
work has emphasized the need for models to capitalize on contextual features,
beyond lexical and syntactic cues present in utterances. For example, different
speakers will tend to employ sarcasm regarding different subjects and, thus,
sarcasm detection models ought to encode such speaker information. Current
methods have achieved this by way of laborious feature engineering. By
contrast, we propose to automatically learn and then exploit user embeddings,
to be used in concert with lexical signals to recognize sarcasm. Our approach
does not require elaborate feature engineering (and concomitant data scraping);
fitting user embeddings requires only the text from their previous posts. The
experimental results show that our model outperforms a state-of-the-art
approach leveraging an extensive set of carefully crafted features.
