Compared with rule-based approaches, neural models (Yuan et al., 2017) can generate more fluent and grammatical questions.
question generation is a one-to-many sequence generation problem, i.e., several aspects can be asked given a sentence, which confuses the model during train and prevents concrete automatic evaluation.