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.

翻译：讽刺识别具有挑战性，因为它需要理解与词语字面意义相反或不同的真实意图。先前的研究通过开发一系列为模型提供更丰富上下文（例如情感或文化细微差别）的方法来应对这一挑战。尽管这些方法各自被证明有效，但尚无研究系统评估它们的集体有效性。因此，额外上下文能在多大程度上提升讽刺识别仍不明确。本文探讨现有方法通过将更多上下文融入模型所带来的改进。为此，我们开发了一个可集成多种上下文线索并测试不同方法的框架。在三个讽刺识别基准测试上采用四种方法进行评估时，我们不仅取得了现有最先进的性能，还展示了依次添加更多上下文的好处。同时，我们也识别出使用更多上下文的固有缺陷，强调在追求更优结果的过程中，模型可能需采纳社会偏见。