  Recent work in automated sarcasm detection has placed a heavy focus on
context and meta-data. Whilst certain utterances indeed require background
knowledge and commonsense reasoning, previous works have only explored shallow
models for capturing the lexical, syntactic and semantic cues present within a
text. In this paper, we propose a deep 56 layer network, implemented with dense
connectivity to model the isolated utterance and extract richer features
therein. We compare our approach against recent state-of-the-art architectures
which make considerable use of extrinsic information, and demonstrate
competitive results whilst using only the local features of the text. Further,
we provide an analysis of the dependency of prior convolution outputs in
generating the final feature maps. Finally a case study is presented,
supporting that our approach accurately classifies additional uses of clear
sarcasm, which a standard CNN misclassifies.
