Before revision:  We propose to disentangle counterfactual generation from its use cases, i.e., gather general-purpose counterfactuals first, and then select them for specific applications. We frame the automated counterfactual generation as text generation, and finetune GPT-2 into a generator , Polyjuice, which produces fluent and diverse counterfactuals.
After revision:  We propose to disentangle counterfactual generation from its use cases, i.e., gather general-purpose counterfactual generator that allows for control over perturbation types and locations, trained by finetuning GPT-2 into a generator , Polyjuice, which produces fluent and diverse counterfactuals.