  Sarcasm recognition is challenging because it needs an understanding of the
true intention, which is opposite to or different from the literal meaning of
the words. Prior work has addressed this challenge by developing a series of
methods that provide richer $contexts$, e.g., sentiment or cultural nuances, to
models. While shown to be effective individually, no study has systematically
evaluated their collective effectiveness. As a result, it remains unclear to
what extent additional contexts can improve sarcasm recognition. In this work,
we explore the improvements that existing methods bring by incorporating more
contexts into a model. To this end, we develop a framework where we can
integrate multiple contextual cues and test different approaches. In evaluation
with four approaches on three sarcasm recognition benchmarks, we achieve
existing state-of-the-art performances and also demonstrate the benefits of
sequentially adding more contexts. We also identify inherent drawbacks of using
more contexts, highlighting that in the pursuit of even better results, the
model may need to adopt societal biases.
