  Recognizing sarcasm often requires a deep understanding of multiple sources
of information, including the utterance, the conversational context, and real
world facts. Most of the current sarcasm detection systems consider only the
utterance in isolation. There are some limited attempts toward taking into
account the conversational context. In this paper, we propose an interpretable
end-to-end model that combines information from both the utterance and the
conversational context to detect sarcasm, and demonstrate its effectiveness
through empirical evaluations. We also study the behavior of the proposed model
to provide explanations for the model's decisions. Importantly, our model is
capable of determining the impact of utterance and conversational context on
the model's decisions. Finally, we provide an ablation study to illustrate the
impact of different components of the proposed model.

Computation and Language;Artificial Intelligence;Machine Learning