HypoGen：常识与反事实知识的夸张生成
HypoGen: Hyperbole Generation with Commonsense and Counterfactual Knowledge
摘要：
夸张是一种故意的和创造性的夸张，不能从字面上理解。尽管它在日常生活中无处不在，计算探索的双曲线是稀缺的。在本文中，我们解决了探索不足和具有挑战性的任务：夸张级生成。我们从一个有代表性的强化句法模式开始，系统地研究了这种夸张中每个成分之间的语义（常识和反事实）关系。接下来，我们利用COMET和反向COMET模型来进行常识和反事实推理。然后，我们根据我们从模式中的发现生成多个夸张候选，并训练神经分类器来排名和选择高质量的夸张。自动和人工评估表明，我们的生成方法能够创造性地生成双曲线，具有高成功率和强度分数。
英文摘要：
A hyperbole is an intentional and creative exaggeration not to be taken literally. Despite its ubiquity in daily life, the computational explorations of hyperboles are scarce. In this paper, we tackle the under-explored and challenging task: sentence-level hyperbole generation. We start with a representative syntactic pattern for intensification and systematically study the semantic (commonsense and counterfactual) relationships between each component in such hyperboles. Next, we leverage the COMeT and reverse COMeT models to do commonsense and counterfactual inference. We then generate multiple hyperbole candidates based on our findings from the pattern, and train neural classifiers to rank and select high-quality hyperboles. Automatic and human evaluations show that our generation method is able to generate hyperboles creatively with high success rate and intensity scores.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
