论文标题
$ r^3 $：以常识知识的讽刺产生讽刺和排名
$R^3$: Reverse, Retrieve, and Rank for Sarcasm Generation with Commonsense Knowledge
论文作者
论文摘要
我们提出了一种基于非释放输入句子的讽刺产生的无监督方法。我们的方法采用检索和编辑框架来实例化讽刺的两个主要特征：价值逆转和语义不一致与可以包括说话者和听众之间共享常识或世界知识的上下文。尽管先前的讽刺产生作品主要集中在上下文不一致上，但我们表明，基于常识性知识的价值逆转和语义不一致会产生更高质量的讽刺。人类评估表明，我们的系统在34％的时间内产生的讽刺性比人类注释者更好，并且比增强的混合基线的时间更好。
We propose an unsupervised approach for sarcasm generation based on a non-sarcastic input sentence. Our method employs a retrieve-and-edit framework to instantiate two major characteristics of sarcasm: reversal of valence and semantic incongruity with the context which could include shared commonsense or world knowledge between the speaker and the listener. While prior works on sarcasm generation predominantly focus on context incongruity, we show that combining valence reversal and semantic incongruity based on the commonsense knowledge generates sarcasm of higher quality. Human evaluation shows that our system generates sarcasm better than human annotators 34% of the time, and better than a reinforced hybrid baseline 90% of the time.
