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A hyperbole is an intentional and creative exaggeration not to be takenliterally. Despite its ubiquity in daily life, the computational explorationsof hyperboles are scarce. In this paper, we tackle the under-explored andchallenging task: sentence-level hyperbole generation. We start with arepresentative syntactic pattern for intensification and systematically studythe semantic (commonsense and counterfactual) relationships between eachcomponent in such hyperboles. Next, we leverage the COMeT and reverse COMeTmodels to do commonsense and counterfactual inference. We then generatemultiple hyperbole candidates based on our findings from the pattern, and trainneural classifiers to rank and select high-quality hyperboles. Automatic andhuman evaluations show that our generation method is able to generatehyperboles creatively with high success rate and intensity scores.

HypoGen: Hyperbole Generation with Commonsense and Counterfactual Knowledge