Sarcasm is a form of speech in which speakers say the opposite of what they truly mean in order to convey a strong sentiment. In other words, "Sarcasm is the giant chasm between what I say, and the person who doesn't get it.". In this paper we present the novel task of sarcasm interpretation, defined as the generation of a non-sarcastic utterance conveying the same message as the original sarcastic one. We introduce a novel dataset of 3000 sarcastic tweets, each interpreted by five human judges. Addressing the task as monolingual machine translation (MT), we experiment with MT algorithms and evaluation measures. We then present SIGN: an MT based sarcasm interpretation algorithm that targets sentiment words, a defining element of textual sarcasm. We show that while the scores of n-gram based automatic measures are similar for all interpretation models, SIGN's interpretations are scored higher by humans for adequacy and sentiment polarity. We conclude with a discussion on future research directions for our new task.

本文提出了讽刺解释的翻译任务，并介绍了一个包含3000个讽刺推文的数据集和一个基于机器翻译算法的讽刺解释算法：SIGN。该算法针对情感词等定义性元素，展示了让人比其他解释模型更高的充分性和情感极性分数。此外，本文还讨论了未来研究方向。

Sarcasm SIGN: 基于情感的单语机器翻译解析讽刺