When Do "More Contexts" Help with Sarcasm Recognition?
This work addresses the problem of improving sarcasm recognition for natural language processing applications, but it is incremental as it builds on prior methods without introducing a new paradigm.
The paper systematically evaluates the collective effectiveness of adding multiple contextual cues for sarcasm recognition, achieving state-of-the-art performance on three benchmarks and identifying drawbacks such as increased societal biases.
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.